dataset_creation/degradation_analysis_and_split.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[   INFO   ] MusicExtractorSVM: no classifier models were configured by default\n"
     ]
    }
   ],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import json\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n",
    "\n",
    "from glob import glob\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "code_dir = os.path.dirname(os.getcwd())\n",
    "sys.path.append(code_dir)\n",
    "\n",
    "from nmfp import audio_processing\n",
    "\n",
    "import IPython.display as ipd\n",
    "\n",
    "fs = 8000\n",
    "\n",
    "fma_dir = \"point_to_your_fma+dir\"\n",
    "\n",
    "datasets_dir = \"../../datasets/\"\n",
    "fma_8k_dir = os.path.join(datasets_dir, \"fma-wav_8khz_16bit\", \"audio\")\n",
    "nafp_dataset_dir  = os.path.join(datasets_dir, \"neural-audio-fp-dataset\")\n",
    "nafp_pp_dataset_dir  = os.path.join(datasets_dir, \"nafp_pp-dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Music of NAFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n",
      "500\n",
      "1000\n"
     ]
    }
   ],
   "source": [
    "nafp_music_dir = os.path.join(nafp_dataset_dir, \"music\")\n",
    "\n",
    "nafp_music_train_dir = os.path.join(nafp_music_dir, \"train\")\n",
    "nafp_music_train_paths = sorted(glob(os.path.join(nafp_music_train_dir, \"**/*.wav\"), recursive=True))\n",
    "\n",
    "# We deleted these files to save disk space\n",
    "# test_dummy_dir = os.path.join(nafp_music_dir, \"test-dummy-db-100k-full\") #/from_fma_large10k-30s_for_mini/\n",
    "# test_dummy_paths = glob(os.path.join(test_dummy_dir, \"**/*.wav\"), recursive=True)\n",
    "# print(len(test_dummy_paths))\n",
    "\n",
    "nafp_music_test_query_dir = os.path.join(nafp_music_dir, \"test-query-db-500-30s\")\n",
    "\n",
    "nafp_music_db_dir = os.path.join(nafp_music_test_query_dir, \"db\")\n",
    "nafp_music_db_paths = sorted(glob(os.path.join(nafp_music_db_dir, \"**/*.wav\"), recursive=True))\n",
    "print(len(nafp_music_db_paths))\n",
    "\n",
    "nafp_music_query_dir = os.path.join(nafp_music_test_query_dir, \"query\")\n",
    "nafp_music_query_paths = sorted(glob(os.path.join(nafp_music_query_dir, \"**/*.wav\"), recursive=True))\n",
    "print(len(nafp_music_query_paths))\n",
    "\n",
    "nafp_music_query_fixed_dir = os.path.join(nafp_music_test_query_dir, \"query_fixed_SNR\")\n",
    "nafp_music_query_fixed_paths = sorted(glob(os.path.join(nafp_music_query_fixed_dir, \"**/*.wav\"), recursive=True))\n",
    "print(len(nafp_music_query_fixed_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# i = 400\n",
    "\n",
    "# path = db_paths[i]\n",
    "# print(path)\n",
    "# ipd.display(ipd.Audio(path))\n",
    "\n",
    "# path = query_paths[i]\n",
    "# print(path)\n",
    "# ipd.display(ipd.Audio(path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background Noise Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NAFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1246 BG Training files\n",
      "310 BG Testing files\n"
     ]
    }
   ],
   "source": [
    "nafp_bg_dir = os.path.join(nafp_dataset_dir, \"aug\", \"bg\")\n",
    "\n",
    "nafp_bg_tr_dir = os.path.join(nafp_bg_dir, \"tr\")\n",
    "nafp_bg_tr_paths = glob(os.path.join(nafp_bg_tr_dir, \"*/*.wav\"))\n",
    "print(f\"{len(nafp_bg_tr_paths)} BG Training files\")\n",
    "\n",
    "nafp_bg_ts_dir = os.path.join(nafp_bg_dir, \"ts\")\n",
    "nafp_bg_ts_paths = glob(os.path.join(nafp_bg_ts_dir, \"*/*.wav\"))\n",
    "print(f\"{len(nafp_bg_ts_paths)} BG Testing files\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quantitative Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min: 10.0 s, Max: 438.077 s, Mean: 15.256609249598716 s, Median: 10.0 s\n",
      "Total: 316.82891875 minutes\n",
      "Min: 10.0 s, Max: 300.0 s, Mean: 13.498117741935484 s, Median: 10.0 s\n",
      "Total: 69.74027500000001 minutes\n"
     ]
    }
   ],
   "source": [
    "nafp_bg_tr_path_durations = []\n",
    "for bg_path in nafp_bg_tr_paths:\n",
    "    bg_audio = audio_processing.load_wav(bg_path, fs=fs)\n",
    "    nafp_bg_tr_path_durations.append([bg_path, len(bg_audio)/fs])\n",
    "nafp_bg_tr_path_durations = sorted(nafp_bg_tr_path_durations, key=lambda x: x[1])\n",
    "nafp_bg_tr_path_durations = np.array([dur for _,dur in nafp_bg_tr_path_durations])\n",
    "print(f\"Min: {nafp_bg_tr_path_durations.min()} s, Max: {nafp_bg_tr_path_durations.max()} s, Mean: {nafp_bg_tr_path_durations.mean()} s, Median: {np.median(nafp_bg_tr_path_durations)} s\")\n",
    "print(f\"Total: {nafp_bg_tr_path_durations.sum()/60} minutes\")\n",
    "\n",
    "nafp_bg_ts_path_durations = []\n",
    "for bg_path in nafp_bg_ts_paths:\n",
    "    bg_audio = audio_processing.load_wav(bg_path, fs=fs)\n",
    "    nafp_bg_ts_path_durations.append([bg_path, len(bg_audio)/fs])\n",
    "nafp_bg_ts_path_durations = sorted(nafp_bg_ts_path_durations, key=lambda x: x[1])\n",
    "nafp_bg_ts_path_durations = np.array([dur for _,dur in nafp_bg_ts_path_durations])\n",
    "print(f\"Min: {nafp_bg_ts_path_durations.min()} s, Max: {nafp_bg_ts_path_durations.max()} s, Mean: {nafp_bg_ts_path_durations.mean()} s, Median: {np.median(nafp_bg_ts_path_durations)} s\")\n",
    "print(f\"Total: {nafp_bg_ts_path_durations.sum()/60} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['audioset_subway_metro_underground_not_music', 'random_noise', 'pub_noise_8k']\n",
      "1224\n",
      "12240.0\n",
      "896\n",
      "896.0\n",
      "22\n",
      "6769.735125000001\n",
      "331.7622520833333\n"
     ]
    }
   ],
   "source": [
    "print(os.listdir(nafp_bg_tr_dir))\n",
    "\n",
    "nafp_bg_tr_subway_dir = os.path.join(nafp_bg_tr_dir, \"audioset_subway_metro_underground_not_music\")\n",
    "nafp_bg_tr_subway_paths = sorted(glob(os.path.join(nafp_bg_tr_subway_dir, \"*.wav\")))\n",
    "print(len(nafp_bg_tr_subway_paths))\n",
    "nafp_bg_tr_subway_durations = []\n",
    "for _path in nafp_bg_tr_subway_paths:\n",
    "    audio_len = audio_processing.check_wav_file(_path, fs=fs)\n",
    "    nafp_bg_tr_subway_durations.append(audio_len/fs)\n",
    "print(sum(nafp_bg_tr_subway_durations))\n",
    "\n",
    "nafp_bg_tr_noise_dir = os.path.join(nafp_bg_tr_dir, \"random_noise\")\n",
    "nafp_bg_tr_noise_paths = sorted(glob(os.path.join(nafp_bg_tr_noise_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "print(len(nafp_bg_tr_noise_paths))\n",
    "nafp_bg_tr_noise_durations = []\n",
    "for _path in nafp_bg_tr_noise_paths:\n",
    "    audio_len = audio_processing.check_wav_file(_path, fs=fs)\n",
    "    nafp_bg_tr_noise_durations.append(audio_len/fs)\n",
    "print(sum(nafp_bg_tr_noise_durations))\n",
    "\n",
    "nafp_bg_tr_pub_dir = os.path.join(nafp_bg_tr_dir, \"pub_noise_8k\")\n",
    "nafp_bg_tr_pub_paths = sorted(glob(os.path.join(nafp_bg_tr_pub_dir, \"*.wav\")))\n",
    "print(len(nafp_bg_tr_pub_paths))\n",
    "nafp_bg_tr_pub_durations = []\n",
    "for _path in nafp_bg_tr_pub_paths:\n",
    "    audio_len = audio_processing.check_wav_file(_path, fs=fs)\n",
    "    nafp_bg_tr_pub_durations.append(audio_len/fs)\n",
    "print(sum(nafp_bg_tr_pub_durations))\n",
    "\n",
    "print((sum(nafp_bg_tr_subway_durations)+sum(nafp_bg_tr_noise_durations)+sum(nafp_bg_tr_pub_durations))/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['audioset_subway_metro_underground_not_music', 'random_noise', 'pub_noise_8k']\n",
      "305\n",
      "3050.0\n",
      "222\n",
      "222.0\n",
      "5\n",
      "1134.4165\n",
      "73.440275\n"
     ]
    }
   ],
   "source": [
    "print(os.listdir(nafp_bg_ts_dir))\n",
    "\n",
    "nafp_bg_ts_subway_dir = os.path.join(nafp_bg_ts_dir, \"audioset_subway_metro_underground_not_music\")\n",
    "nafp_bg_ts_subway_paths = sorted(glob(os.path.join(nafp_bg_ts_subway_dir, \"*.wav\")))\n",
    "print(len(nafp_bg_ts_subway_paths))\n",
    "nafp_bg_ts_subway_durations = []\n",
    "for _path in nafp_bg_ts_subway_paths:\n",
    "    audio_len = audio_processing.check_wav_file(_path, fs=fs)\n",
    "    nafp_bg_ts_subway_durations.append(audio_len/fs)\n",
    "print(sum(nafp_bg_ts_subway_durations))\n",
    "\n",
    "nafp_bg_ts_noise_dir = os.path.join(nafp_bg_ts_dir, \"random_noise\")\n",
    "nafp_bg_ts_noise_paths = sorted(glob(os.path.join(nafp_bg_ts_noise_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "print(len(nafp_bg_ts_noise_paths))\n",
    "nafp_bg_ts_noise_durations = []\n",
    "for _path in nafp_bg_ts_noise_paths:\n",
    "    audio_len = audio_processing.check_wav_file(_path, fs=fs)\n",
    "    nafp_bg_ts_noise_durations.append(audio_len/fs)\n",
    "print(sum(nafp_bg_ts_noise_durations))\n",
    "\n",
    "nafp_bg_ts_pub_dir = os.path.join(nafp_bg_ts_dir, \"pub_noise_8k\")\n",
    "nafp_bg_ts_pub_paths = sorted(glob(os.path.join(nafp_bg_ts_pub_dir, \"*.wav\")))\n",
    "print(len(nafp_bg_ts_pub_paths))\n",
    "nafp_bg_ts_pub_durations = []\n",
    "for _path in nafp_bg_ts_pub_paths:\n",
    "    audio_len = audio_processing.check_wav_file(_path, fs=fs)\n",
    "    nafp_bg_ts_pub_durations.append(audio_len/fs)\n",
    "print(sum(nafp_bg_ts_pub_durations))\n",
    "\n",
    "print((sum(nafp_bg_ts_subway_durations)+sum(nafp_bg_ts_noise_durations)+sum(nafp_bg_ts_pub_durations))/60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TUT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1170\n",
      "390\n"
     ]
    }
   ],
   "source": [
    "# tut_dataset_dir = os.path.join(datasets_dir, \"TUT-acoustic-scenes-2016\")\n",
    "tut_dataset_dir = os.path.join(datasets_dir, \"TUT-acoustic-scenes-2016-8khz_16bit\")\n",
    "\n",
    "tut_dev_dir = os.path.join(tut_dataset_dir, \"TUT-acoustic-scenes-2016-development\")\n",
    "tut_dev_paths = sorted(glob(os.path.join(tut_dev_dir, \"**/*.wav\"), recursive=True))\n",
    "print(len(tut_dev_paths))\n",
    "\n",
    "tut_eval_dir = os.path.join(tut_dataset_dir, \"TUT-acoustic-scenes-2016-evaluation\")\n",
    "tut_eval_paths = sorted(glob(os.path.join(tut_eval_dir, \"**/*.wav\"), recursive=True))\n",
    "print(len(tut_eval_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1170, 2)\n"
     ]
    },
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     "execution_count": 5,
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   "source": [
    "tut_dev_csv = os.path.join(tut_dev_dir, \"meta.txt\")\n",
    "tut_dev_df = pd.read_csv(tut_dev_csv, sep=\"\\t\", header=None)\n",
    "print(tut_dev_df.shape)\n",
    "tut_dev_df[0] = tut_dev_df[0].apply(lambda x: x.split(\"/\")[1]) # remove the path\n",
    "tut_dev_df.head()"
   ]
  },
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   "cell_type": "code",
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   "outputs": [
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     "text": [
      "(390, 2)\n"
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       "      <td>forest_path</td>\n",
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       "      <td>beach</td>\n",
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "tut_eval_csv = os.path.join(tut_eval_dir, \"meta.txt\")\n",
    "tut_eval_df = pd.read_csv(tut_eval_csv, sep=\"\\t\", header=None)\n",
    "tut_eval_df[0] = tut_eval_df[0].apply(lambda x: x.split(\"/\")[1]) # remove the path\n",
    "print(tut_eval_df.shape)\n",
    "tut_eval_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# path = random.choice(tut_dev_paths)\n",
    "# print(path)\n",
    "# print(tut_dev_df[tut_dev_df[0] == os.path.split(path)[-1]])\n",
    "# ipd.display(ipd.Audio(path))\n",
    "\n",
    "# path = random.choice(tut_eval_paths)\n",
    "# print(path)\n",
    "# print(tut_eval_df[tut_eval_df[0] == os.path.split(path)[-1]])\n",
    "# ipd.display(ipd.Audio(path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quantitative Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1170\n",
      "35100.0\n",
      "30.0\n",
      "30.0\n",
      "30.0\n"
     ]
    }
   ],
   "source": [
    "print(len(tut_dev_paths))\n",
    "tut_dev_durations = []\n",
    "for _path in tut_dev_paths:\n",
    "    audio_len = audio_processing.check_wav_file(_path, fs=fs)\n",
    "    tut_dev_durations.append(audio_len/fs)\n",
    "print(sum(tut_dev_durations))\n",
    "print(np.mean(tut_dev_durations))\n",
    "print(np.median(tut_dev_durations))\n",
    "print(np.max(tut_dev_durations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "390\n",
      "11700.0\n",
      "30.0\n",
      "30.0\n",
      "30.0\n"
     ]
    }
   ],
   "source": [
    "print(len(tut_eval_paths))\n",
    "tut_eval_durations = []\n",
    "for _path in tut_eval_paths:\n",
    "    audio_len = audio_processing.check_wav_file(_path, fs=fs)\n",
    "    tut_eval_durations.append(audio_len/fs)\n",
    "print(sum(tut_eval_durations))\n",
    "print(np.mean(tut_eval_durations))\n",
    "print(np.median(tut_eval_durations))\n",
    "print(np.max(tut_eval_durations))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Room Impulse Response Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OpenAIR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "143\n",
      "28\n",
      "elveden-hall-suffolk-england\n",
      "falkland-palace-royal-tennis-court\n",
      "gill-heads-mine\n",
      "hamilton-mausoleum\n",
      "innocent-railway-tunnel\n",
      "koli-national-park-summer\n",
      "koli-national-park-winter\n",
      "lady-chapel-st-albans-cathedral\n",
      "maes-howe\n",
      "r1-nuclear-reactor-hall\n",
      "saint-lawrence-church-molenbeek-wersbeek-belgium\n",
      "shrine-and-parish-church-all-saints-north-street-_\n",
      "spokane-womans-club\n",
      "sports-centre-university-york\n",
      "spring-lane-building-university-york\n",
      "st-andrews-church\n",
      "st-margarets-church-ncem-5-piece-band-spatial-measurements\n",
      "st-matthews-church-walsall\n",
      "st-patricks-church-patrington\n",
      "stairway-university-york\n",
      "terrys-factory-warehouse\n",
      "terrys-typing-room\n",
      "trollers-gill\n",
      "tyndall-bruce-monument\n",
      "usina-del-arte-symphony-hall\n",
      "waveguide-web-example-audio\n",
      "wheldrake-wood\n",
      "york-minster\n"
     ]
    }
   ],
   "source": [
    "open_air_8k_dir = os.path.join(datasets_dir, \"IR\", \"room\", \"OPENAIR-mono_and_stereo-8khz_16bit_mono\")\n",
    "open_air_8k_paths = sorted(glob(os.path.join(open_air_8k_dir, \"**/*.wav\"), recursive=True))\n",
    "print(len(open_air_8k_paths))\n",
    "\n",
    "open_air_8k_rooms = sorted([d for d in os.listdir(open_air_8k_dir) if os.path.isdir(os.path.join(open_air_8k_dir, d))])\n",
    "print(len(open_air_8k_rooms))\n",
    "print('\\n'.join(open_air_8k_rooms))\n",
    "\n",
    "open_air_8k_names = sorted([os.path.basename(path) for path in open_air_8k_paths])\n",
    "# print(len(open_air_8k_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analyze IR durations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 143 IRs\n",
      "Min duration: 1.0000 s\n",
      "Max duration: 22.6704 s\n",
      "Mean duration: 5.2280 s\n",
      "Std duration: 3.6559 s\n",
      "Median duration: 5.0000 s\n",
      "Total duration: 12.4600 m\n",
      "141 files are longer than 1.0 s\n",
      "Total duration of these files: 12.4267 m\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T = 1.0\n",
    "\n",
    "openair_8k_ir_durations = []\n",
    "for ir_path in open_air_8k_paths:\n",
    "    ir_len = audio_processing.check_wav_file(ir_path, fs=fs)\n",
    "    openair_8k_ir_durations.append(ir_len/fs)\n",
    "print(f\"There are {len(openair_8k_ir_durations)} IRs\")\n",
    "openair_8k_ir_durations = np.array(openair_8k_ir_durations)\n",
    "print(f\"Min duration: {openair_8k_ir_durations.min():.4f} s\")\n",
    "print(f\"Max duration: {openair_8k_ir_durations.max():.4f} s\")\n",
    "print(f\"Mean duration: {openair_8k_ir_durations.mean():.4f} s\")\n",
    "print(f\"Std duration: {openair_8k_ir_durations.std():.4f} s\")\n",
    "print(f\"Median duration: {np.median(openair_8k_ir_durations):.4f} s\")\n",
    "print(f\"Total duration: {openair_8k_ir_durations.sum()/60:.4f} m\")\n",
    "\n",
    "open_air_8k_long_ir_durs = [dur for dur in openair_8k_ir_durations if dur > T]\n",
    "print(f\"{len(open_air_8k_long_ir_durs)} files are longer than {T} s\")\n",
    "print(f\"Total duration of these files: {sum(open_air_8k_long_ir_durs)/60:.4f} m\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(18, 5), constrained_layout=True)\n",
    "fig.suptitle(\"OpenAIR Mono and Stereo IRs Duration Histogram\")\n",
    "\n",
    "x_max = max(openair_8k_ir_durations)\n",
    "\n",
    "ax.hist(openair_8k_ir_durations, bins=np.arange(0, x_max+0.5, 0.5))\n",
    "# ax.set_title(\"Train Set\")\n",
    "ax.set_xticks(np.arange(0, x_max+0.5, 0.5))\n",
    "ax.set_xlim([0,x_max+0.5])\n",
    "ax.set_ylabel(\"Count\")\n",
    "ax.set_yticks(np.arange(0, 46, 5))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "open_air_8k_room_durations = []\n",
    "for room in open_air_8k_rooms:\n",
    "    room_dir = os.path.join(open_air_8k_dir, room)\n",
    "    room_paths = sorted(glob(os.path.join(room_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "    _durations = []\n",
    "    for _path in room_paths:\n",
    "        audio_len = audio_processing.check_wav_file(_path, fs=fs)\n",
    "        _durations.append(audio_len/fs)\n",
    "    dur = np.min(_durations)\n",
    "    open_air_8k_room_durations.append(dur)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(18, 5), constrained_layout=True)\n",
    "fig.suptitle(\"OpenAIR Rooms with Mono and Stereo IRs Duration Histogram\")\n",
    "\n",
    "x_max = max(open_air_8k_room_durations)\n",
    "ax.hist(open_air_8k_room_durations, bins=np.arange(0, x_max+0.5, 0.5))\n",
    "ax.set_xticks(np.arange(0, x_max+0.5, 0.5))\n",
    "ax.set_xlim([0-0.25,x_max+0.5])\n",
    "ax.set_ylabel(\"Count\")\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listen to the rooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "falkland-palace-royal-tennis-court\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "room = open_air_8k_rooms[1]\n",
    "print(room)\n",
    "room_ir_paths = sorted(glob(os.path.join(open_air_8k_dir, room, \"**/*.wav\"), recursive=True))\n",
    "print(len(room_ir_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "audio_path = nafp_music_db_paths[100]\n",
    "audio = audio_processing.load_wav(audio_path, fs=fs)\n",
    "print(audio_path)\n",
    "ipd.display(ipd.Audio(audio, rate=fs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for ir_path in room_ir_paths:\n",
    "\n",
    "    ir = audio_processing.load_wav(ir_path, fs=fs)\n",
    "    print(os.path.basename(ir_path))\n",
    "    print(np.max(np.abs(ir)))\n",
    "    print(len(ir)/fs)\n",
    "    ipd.display(ipd.Audio(ir, rate=fs))\n",
    "\n",
    "    audio_room = audio_processing.convolve_with_IR(audio, ir)\n",
    "    ipd.display(ipd.Audio(audio_room[:10*fs], rate=fs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find Mono and Stereo Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "604\n",
      "56\n"
     ]
    }
   ],
   "source": [
    "open_air_dir = os.path.join(datasets_dir, \"IR\", \"room\", \"OPENAIR\", \"IRs\")\n",
    "open_air_ir_paths = sorted(glob(os.path.join(open_air_dir, \"**/*.wav\"), recursive=True))\n",
    "print(len(open_air_ir_paths))\n",
    "\n",
    "open_air_rooms = sorted([d for d in os.listdir(open_air_dir) if os.path.isdir(os.path.join(open_air_dir, d))])\n",
    "print(len(open_air_rooms))\n",
    "# print('\\n'.join(open_air_rooms))\n",
    "\n",
    "open_air_ir_names = sorted([os.path.basename(path) for path in open_air_ir_paths])\n",
    "# print(len(open_air_ir_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['stereo']\n",
      "['b-format']\n",
      "['b-format']\n",
      "['b-format']\n",
      "['b-format']\n",
      "['clifford-tower-IR-Bformat']\n",
      "['b-format', 'mono']\n",
      "['b-format']\n",
      "['stereo']\n",
      "['b-format']\n",
      "['b-format', 'mono', 'stereo', 'surround-5-1']\n",
      "['IR_ScaleModel', 'IR_Streched_ScaleModel']\n",
      "['b-format']\n",
      "['b-format', 'mono']\n",
      "['b-format', 'stereo', 'surround-5-1']\n",
      "['HH-3order-Ambisonic-16channels', 'HH-Bformat']\n",
      "['b-format']\n",
      "['b-format']\n",
      "['auralizations']\n",
      "['Auralization Results-20220429T115329Z-001']\n",
      "['Auralization Results']\n",
      "['b-format', 'mono']\n",
      "['b-format']\n",
      "['b-format', 'mono']\n",
      "['b-format', 'mono']\n",
      "['b-format', 'mono', 'stereo', 'surround-5-1']\n",
      "['B-Format', 'Examples', 'Images', 'Virtual Recreation']\n",
      "['b-format', 'stereo']\n",
      "['b-format']\n",
      "['PL001-3order-Ambisonic-16channels', 'PL001-bformat']\n",
      "['b-format', 'mono', 'stereo', 'surround-5-1']\n",
      "['b-format']\n",
      "['stereo']\n",
      "['b-format', 'mono']\n",
      "['mono']\n",
      "['stereo']\n",
      "['b-format', 'mono', 'stereo', 'surround-5-1']\n",
      "['b-format', 'stereo']\n",
      "['b-format', 'stereo']\n",
      "['stereo']\n",
      "['b-format', 'Video']\n",
      "['b-format', 'stereo']\n",
      "['b-format', 'stereo']\n",
      "['mono']\n",
      "['b-format']\n",
      "['b-format', 'stereo']\n",
      "['stereo']\n",
      "['b-format', 'mono', 'stereo', 'surround-5-1']\n",
      "['b-format', 'mono', 'stereo', 'surround-5-1']\n",
      "['b-format', 'mono']\n",
      "['b-format', 'stereo']\n",
      "['b-format', 'mono', 'stereo', 'surround-5-1']\n",
      "['b-format', 'mono', 'stereo']\n",
      "['mono']\n",
      "['mono']\n",
      "['IR_M30', 'IR_SF']\n",
      "['b-format']\n",
      "['b-format', 'stereo', 'surround-5-1']\n"
     ]
    }
   ],
   "source": [
    "for root, dirs, files in os.walk(open_air_dir):\n",
    "    if root == open_air_dir:\n",
    "        continue\n",
    "    if dirs:\n",
    "        print(dirs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "wheldrake-wood/IR_M30/ is mono 2 sources 2 receivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "open_air_mono_dirs = []\n",
    "open_air_no_mono_stereo_dirs = []\n",
    "for root, dirs, files in os.walk(open_air_dir):\n",
    "\n",
    "    if root == open_air_dir:\n",
    "        continue\n",
    "    if 'mono' in dirs:\n",
    "        # print(root)\n",
    "        open_air_mono_dirs.append(os.path.join(root, 'mono'))\n",
    "    elif 'stereo' in dirs:\n",
    "        open_air_no_mono_stereo_dirs.append(os.path.join(root, 'stereo'))\n",
    "\n",
    "    # edge case of mono\n",
    "    if 'IR_M30' in dirs:\n",
    "        open_air_mono_dirs.append(os.path.join(root, 'IR_M30'))\n",
    "\n",
    "print(len(open_air_mono_dirs))\n",
    "print(len(open_air_no_mono_stereo_dirs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86\n",
      "73\n"
     ]
    }
   ],
   "source": [
    "open_air_mono_paths = []\n",
    "for root in open_air_mono_dirs:\n",
    "    open_air_mono_paths.extend(glob(os.path.join(root, '**', \"*.wav\"), recursive=True))\n",
    "open_air_mono_paths = sorted(open_air_mono_paths)\n",
    "open_air_mono_names = [os.path.basename(path) for path in open_air_mono_paths]\n",
    "print(len(open_air_mono_paths))\n",
    "\n",
    "open_air_no_mono_stereo_paths = []\n",
    "for root in open_air_no_mono_stereo_dirs:\n",
    "    open_air_no_mono_stereo_paths.extend(glob(os.path.join(root, '**', \"*.wav\"), recursive=True))\n",
    "open_air_no_mono_stereo_paths = sorted(open_air_no_mono_stereo_paths)\n",
    "open_air_no_mono_stereo_names = [os.path.basename(path) for path in open_air_no_mono_stereo_paths]\n",
    "print(len(open_air_no_mono_stereo_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../datasets/IR/room/OPENAIR-mono/falkland-palace-royal-tennis-court/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/gill-heads-mine/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/innocent-railway-tunnel/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/koli-national-park-summer/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/koli-national-park-winter/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/lady-chapel-st-albans-cathedral/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/r1-nuclear-reactor-hall/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/shrine-and-parish-church-all-saints-north-street-_/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/sports-centre-university-york/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/st-matthews-church-walsall/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/terrys-factory-warehouse/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/terrys-typing-room/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/trollers-gill/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/tyndall-bruce-monument/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/usina-del-arte-symphony-hall/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/waveguide-web-example-audio/mono\n",
      "../../datasets/IR/room/OPENAIR-mono/wheldrake-wood/IR_M30\n"
     ]
    }
   ],
   "source": [
    "openair_monos_new_dir = os.path.join(os.path.dirname(os.path.dirname(open_air_dir)), \"OPENAIR-mono\")\n",
    "os.makedirs(openair_monos_new_dir, exist_ok=True)\n",
    "\n",
    "for ir_dir in open_air_mono_dirs:\n",
    "\n",
    "    # creswell is  empty\n",
    "    # slinky is irrelevant\n",
    "    # virtual-membranes irrelevant\n",
    "    if \"creswell-crags\" in ir_dir or \"slinky-ir\" in ir_dir or \"virtual-membranes\" in ir_dir:\n",
    "        continue\n",
    "\n",
    "    structure = os.sep.join(ir_dir.split(os.sep)[-2:])\n",
    "    new_dir = os.path.join(openair_monos_new_dir, structure)\n",
    "    print(new_dir)\n",
    "    os.makedirs(new_dir, exist_ok=True)\n",
    "\n",
    "    ir_paths = sorted(glob(os.path.join(ir_dir, \"*.wav\")))\n",
    "    for path in ir_paths:\n",
    "        # Duplicate 32khz also exist\n",
    "        if \"r1-nuclear-reactor-hall/mono/r1_omni_48k.wav\" in path:\n",
    "            continue\n",
    "        # Only keep this real file\n",
    "        if \"waveguide-web-example-audio\" in path and \"courtyard_recorded_ir.wav\" != os.path.basename(path):\n",
    "            continue\n",
    "        name = os.path.basename(path)\n",
    "        new_path = os.path.join(new_dir, name)\n",
    "        # shutil.copy(path, new_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../datasets/IR/room/OPENAIR-stereo/1st-baptist-nashville/stereo\n",
      "../../datasets/IR/room/OPENAIR-stereo/elveden-hall-suffolk-england/stereo\n",
      "../../datasets/IR/room/OPENAIR-stereo/hamilton-mausoleum/stereo\n",
      "../../datasets/IR/room/OPENAIR-stereo/maes-howe/stereo\n",
      "../../datasets/IR/room/OPENAIR-stereo/saint-lawrence-church-molenbeek-wersbeek-belgium/stereo\n",
      "../../datasets/IR/room/OPENAIR-stereo/spokane-womans-club/stereo\n",
      "../../datasets/IR/room/OPENAIR-stereo/spring-lane-building-university-york/stereo\n",
      "../../datasets/IR/room/OPENAIR-stereo/st-andrews-church/stereo\n",
      "../../datasets/IR/room/OPENAIR-stereo/st-georges-episcopal-church/stereo\n",
      "../../datasets/IR/room/OPENAIR-stereo/st-margarets-church-ncem-5-piece-band-spatial-measurements/stereo\n",
      "../../datasets/IR/room/OPENAIR-stereo/st-marys-abbey-reconstruction/stereo\n",
      "../../datasets/IR/room/OPENAIR-stereo/st-patricks-church-patrington/stereo\n",
      "../../datasets/IR/room/OPENAIR-stereo/stairway-university-york/stereo\n",
      "../../datasets/IR/room/OPENAIR-stereo/york-minster/stereo\n"
     ]
    }
   ],
   "source": [
    "openair_stereos_new_dir = os.path.join(os.path.dirname(os.path.dirname(open_air_dir)), \"OPENAIR-stereo\")\n",
    "os.makedirs(openair_stereos_new_dir, exist_ok=True)\n",
    "\n",
    "for ir_dir in open_air_no_mono_stereo_dirs:\n",
    "\n",
    "    if \"tvisongur-sound-sculpture-iceland-model\" in ir_dir:\n",
    "        continue\n",
    "\n",
    "    structure = os.sep.join(ir_dir.split(os.sep)[-2:])\n",
    "    new_dir = os.path.join(openair_stereos_new_dir, structure)\n",
    "    print(new_dir)\n",
    "    os.makedirs(new_dir, exist_ok=True)\n",
    "\n",
    "    ir_paths = sorted(glob(os.path.join(ir_dir, \"*.wav\")))\n",
    "    for path in ir_paths:\n",
    "        name = os.path.basename(path)\n",
    "        new_path = os.path.join(new_dir, name)\n",
    "        # print(new_path)\n",
    "        # shutil.copy(path, new_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aachen IR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "224\n"
     ]
    }
   ],
   "source": [
    "aachen_ir_dir = os.path.join(datasets_dir, \"IR\", \"room\", \"AIR_1_4\")\n",
    "aachen_ir_paths = sorted(glob(os.path.join(aachen_ir_dir, \"**/*.wav\"), recursive=True))\n",
    "print(len(aachen_ir_paths))\n",
    "\n",
    "aachen_ir_names = sorted([os.path.basename(path) for path in aachen_ir_paths])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60\n",
      "6\n",
      "aula_carolina 11\n",
      "booth 6\n",
      "lecture 12\n",
      "meeting 10\n",
      "office 6\n",
      "stairway 15\n"
     ]
    }
   ],
   "source": [
    "aachen_bin_ir_dir = os.path.join(datasets_dir, \"IR\", \"room\", \"AIR_1_4-Binaural_no_dummy_head-from_mat-8khz_16bit\")\n",
    "\n",
    "aachen_bin_ir_paths = sorted(glob(os.path.join(aachen_bin_ir_dir, \"**/*.wav\"), recursive=True))\n",
    "print(len(aachen_bin_ir_paths))\n",
    "\n",
    "aachen_bin_ir_rooms = sorted([d for d in os.listdir(aachen_bin_ir_dir) if os.path.isdir(os.path.join(aachen_bin_ir_dir, d))])\n",
    "print(len(aachen_bin_ir_rooms))\n",
    "for room in aachen_bin_ir_rooms:\n",
    "    room_ir_paths = sorted(glob(os.path.join(aachen_bin_ir_dir, room, \"**/*.wav\"), recursive=True))\n",
    "    print(room, len(room_ir_paths))\n",
    "\n",
    "aachen_bin_ir_names = sorted([os.path.basename(path) for path in aachen_bin_ir_paths])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analyze IR durations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aachen_bin_ir_durations = []\n",
    "for ir_path in aachen_bin_ir_paths:\n",
    "    dur = audio_processing.check_wav_file(ir_path, fs=fs) / fs\n",
    "    aachen_bin_ir_durations.append(dur)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(18, 5), constrained_layout=True)\n",
    "fig.suptitle(\"AachenIR Dataset BRIR Duration Histogram\")\n",
    "\n",
    "x_max = max(aachen_bin_ir_durations)\n",
    "ax.hist(aachen_bin_ir_durations, bins=np.arange(0, x_max+0.5, 0.5))\n",
    "ax.set_xticks(np.arange(0, x_max+0.5, 0.5))\n",
    "ax.set_xlim([0-0.25,x_max+0.5])\n",
    "ax.set_ylabel(\"Count\")\n",
    "ax.set_xlabel(\"Dur(s)\")\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aula_carolina\n",
      "9.987\n",
      "9.987\n",
      "9.987\n",
      "9.986999999999998\n",
      "\n",
      "booth\n",
      "0.682625\n",
      "0.682625\n",
      "0.682625\n",
      "0.6826249999999999\n",
      "\n",
      "lecture\n",
      "1.365375\n",
      "1.365375\n",
      "1.365375\n",
      "1.365375\n",
      "\n",
      "meeting\n",
      "0.682625\n",
      "0.682625\n",
      "0.682625\n",
      "0.682625\n",
      "\n",
      "office\n",
      "0.682625\n",
      "0.682625\n",
      "0.682625\n",
      "0.6826249999999999\n",
      "\n",
      "stairway\n",
      "2.0\n",
      "2.0\n",
      "2.0\n",
      "2.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "aachen_bin_ir_room_durations = []\n",
    "for room in aachen_bin_ir_rooms:\n",
    "    room_ir_paths = sorted(glob(os.path.join(aachen_bin_ir_dir, room, \"**/*.wav\"), recursive=True))\n",
    "    _durations = []\n",
    "    for ir_path in room_ir_paths:\n",
    "        audio_len = audio_processing.check_wav_file(ir_path, fs=fs)\n",
    "        _durations.append(audio_len/fs)\n",
    "    print(room)\n",
    "    print(np.min(_durations))\n",
    "    print(np.max(_durations))\n",
    "    print(np.median(_durations))\n",
    "    print(np.mean(_durations))\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listen to the rooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stairway\n"
     ]
    }
   ],
   "source": [
    "room = aachen_bin_ir_rooms[5]\n",
    "print(room)\n",
    "room_ir_paths = sorted(glob(os.path.join(aachen_bin_ir_dir, room, \"**/*.wav\"), recursive=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "audio_path = nafp_music_db_paths[100]\n",
    "audio = audio_processing.load_wav(audio_path, fs=fs)\n",
    "print(audio_path)\n",
    "ipd.display(ipd.Audio(audio, rate=fs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for ir_path in room_ir_paths:\n",
    "\n",
    "    ir = audio_processing.load_wav(ir_path, fs=fs)\n",
    "    print(os.path.basename(ir_path))\n",
    "    ipd.display(ipd.Audio(ir, rate=fs))\n",
    "\n",
    "    audio_room = audio_processing.convolve_with_IR(audio, ir)\n",
    "    ipd.display(ipd.Audio(audio_room[:5*fs], rate=fs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "270\n"
     ]
    }
   ],
   "source": [
    "mit_ir_dir = os.path.join(datasets_dir, \"IR\", \"room\", \"MIT_Survey-8khz_16bit\")\n",
    "mit_ir_paths = sorted(glob(os.path.join(mit_ir_dir, \"**/*.wav\"), recursive=True))\n",
    "print(len(mit_ir_paths))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analyze IR durations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 270 IRs\n",
      "Min duration: 0.0948 s\n",
      "Max duration: 1.9991 s\n",
      "Mean duration: 0.6445 s\n",
      "Std duration: 0.4690 s\n",
      "Median duration: 0.5611 s\n",
      "Total duration: 2.9004 m\n",
      "46 files are longer than 1.0 s\n",
      "Total duration of these files: 1.1394 m\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T = 1.0\n",
    "\n",
    "mit_ir_durations = []\n",
    "for ir_path in mit_ir_paths:\n",
    "    ir_len = audio_processing.check_wav_file(ir_path, fs=fs)\n",
    "    mit_ir_durations.append(ir_len/fs)\n",
    "# print(\"Train\")\n",
    "print(f\"There are {len(mit_ir_durations)} IRs\")\n",
    "mit_ir_durations = np.array(mit_ir_durations)\n",
    "print(f\"Min duration: {mit_ir_durations.min():.4f} s\")\n",
    "print(f\"Max duration: {mit_ir_durations.max():.4f} s\")\n",
    "print(f\"Mean duration: {mit_ir_durations.mean():.4f} s\")\n",
    "print(f\"Std duration: {mit_ir_durations.std():.4f} s\")\n",
    "print(f\"Median duration: {np.median(mit_ir_durations):.4f} s\")\n",
    "print(f\"Total duration: {mit_ir_durations.sum()/60:.4f} m\")\n",
    "\n",
    "mit_long_ir_durs = [dur for dur in mit_ir_durations if dur > T]\n",
    "print(f\"{len(mit_long_ir_durs)} files are longer than {T} s\")\n",
    "print(f\"Total duration of these files: {sum(mit_long_ir_durs)/60:.4f} m\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(18, 5), constrained_layout=True)\n",
    "fig.suptitle(\"MIT IR Dataset Duration Histogram\")\n",
    "\n",
    "x_max = max(mit_ir_durations)\n",
    "delta_t = 0.25\n",
    "\n",
    "ax.hist(mit_ir_durations, bins=np.arange(0, x_max+delta_t, delta_t))\n",
    "# ax.set_title(\"Train Set\")\n",
    "ax.set_xticks(np.arange(0, x_max+delta_t, delta_t))\n",
    "ax.set_xlim([0-(delta_t/2),x_max+(delta_t/2)])\n",
    "ax.set_ylabel(\"Count\")\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listen to the rooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "audio_path = nafp_music_db_paths[100]\n",
    "audio = audio_processing.load_wav(audio_path, fs=fs)\n",
    "print(audio_path)\n",
    "ipd.display(ipd.Audio(audio, rate=fs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 1.776375 h026_Gym_8txts.wav\n",
      "35 1.982 h036_Bathroom_5txts.wav\n",
      "40 1.999125 h041_TrainStation_SouthStationBoston_4txts.wav\n",
      "41 1.9935 h042_Hallway_ElementarySchool_4txts.wav\n",
      "42 1.98225 h043_Train_BostonTRedLine_4txts.wav\n",
      "48 1.99225 h049_MallFoodCourt_BurlingtonMall_3txts.wav\n",
      "59 1.566125 h060_Office_ConferenceRoom_3txts.wav\n",
      "80 1.987125 h081_Shower_2txts.wav\n",
      "93 1.57425 h094_Campground_CabinLivingroom_2txts.wav\n",
      "127 1.992375 h128_Supermarket_1txts.wav\n",
      "132 1.98125 h133_SubwayStation_ParkStreetBoston_1txts.wav\n",
      "138 1.994875 h139_OutsideStreetsOfBoston_1txts.wav\n",
      "177 1.6505 h178_OfficeFoyer_1txts.wav\n",
      "210 1.993 h211_Stairwell_1txts.wav\n",
      "212 1.868625 h213_SubwayStation_CentralSquareCambridge_1txts.wav\n",
      "231 1.659875 h232_Hallway_MITCampus_1txts.wav\n",
      "249 1.87225 h251_Hallway_MITCampus_1txts.wav\n",
      "253 1.52475 h255_MITCampus_StudentLounge_1txts.wav\n",
      "254 1.993375 h256_Stairwell_1txts.wav\n"
     ]
    }
   ],
   "source": [
    "for i,ir_path in enumerate(mit_ir_paths):\n",
    "    ir_dur = audio_processing.check_wav_file(ir_path, fs=fs) / fs\n",
    "    if ir_dur > 1.5:\n",
    "        print(i,ir_dur,os.path.basename(ir_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ir_path = mit_ir_paths[41]\n",
    "\n",
    "ir = audio_processing.load_wav(ir_path, fs=fs)\n",
    "\n",
    "print('IR')\n",
    "print(os.path.basename(ir_path))\n",
    "print(len(ir)/fs)\n",
    "ipd.display(ipd.Audio(ir, rate=fs))\n",
    "\n",
    "print('Music')\n",
    "ipd.display(ipd.Audio(audio[:10*fs], rate=fs))\n",
    "\n",
    "print('Music in Room')\n",
    "audio_room = audio_processing.convolve_with_IR(audio, ir)\n",
    "ipd.display(ipd.Audio(audio_room[:10*fs], rate=fs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NAFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "345 IR Training files\n",
      "95 IR Testing files\n"
     ]
    }
   ],
   "source": [
    "nafp_ir_dir = os.path.join(nafp_dataset_dir, \"aug/ir\")\n",
    "\n",
    "nafp_ir_tr_dir = os.path.join(nafp_ir_dir, \"tr\")\n",
    "nafp_ir_tr_paths = glob(os.path.join(nafp_ir_tr_dir, \"*.wav\"))\n",
    "print(f\"{len(nafp_ir_tr_paths)} IR Training files\")\n",
    "\n",
    "nafp_ir_ts_dir = os.path.join(nafp_ir_dir, \"ts\")\n",
    "nafp_ir_ts_paths = glob(os.path.join(nafp_ir_ts_dir, \"*.wav\"))\n",
    "print(f\"{len(nafp_ir_ts_paths)} IR Testing files\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analyze IR durations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n",
      "There are 345 IRs\n",
      "Min duration: 0.0015 s\n",
      "Max duration: 20.2800 s\n",
      "Mean duration: 2.0386 s\n",
      "Std duration: 1.7806 s\n",
      "Median duration: 1.7386 s\n",
      "Total duration: 11.7220 m\n",
      "266 files are longer than 1.0 s\n",
      "Total duration of these files: 11.2952 m\n",
      "\n",
      "Test\n",
      "There are 95 IRs\n",
      "Min duration: 0.0019 s\n",
      "Max duration: 23.0108 s\n",
      "Mean duration: 2.1089 s\n",
      "Std duration: 3.2728 s\n",
      "Median duration: 1.2985 s\n",
      "Total duration: 3.3390 m\n",
      "60 files are longer than 1.0 s\n",
      "Total duration of these files: 3.1111 m\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T = 1.0\n",
    "\n",
    "nafp_ir_tr_durations = []\n",
    "for ir_path in nafp_ir_tr_paths:\n",
    "    ir = audio_processing.load_wav(ir_path, fs=fs)\n",
    "    nafp_ir_tr_durations.append(len(ir)/fs)\n",
    "print(\"Train\")\n",
    "print(f\"There are {len(nafp_ir_tr_durations)} IRs\")\n",
    "nafp_ir_tr_durations = np.array(nafp_ir_tr_durations)\n",
    "print(f\"Min duration: {nafp_ir_tr_durations.min():.4f} s\")\n",
    "print(f\"Max duration: {nafp_ir_tr_durations.max():.4f} s\")\n",
    "print(f\"Mean duration: {nafp_ir_tr_durations.mean():.4f} s\")\n",
    "print(f\"Std duration: {nafp_ir_tr_durations.std():.4f} s\")\n",
    "print(f\"Median duration: {np.median(nafp_ir_tr_durations):.4f} s\")\n",
    "print(f\"Total duration: {nafp_ir_tr_durations.sum()/60:.4f} m\")\n",
    "\n",
    "long_ir_tr_durs = [dur for dur in nafp_ir_tr_durations if dur > T]\n",
    "print(f\"{len(long_ir_tr_durs)} files are longer than {T} s\")\n",
    "print(f\"Total duration of these files: {sum(long_ir_tr_durs)/60:.4f} m\")\n",
    "\n",
    "nafp_ir_ts_durations = []\n",
    "for ir_path in nafp_ir_ts_paths:\n",
    "    ir = audio_processing.load_wav(ir_path, fs=fs)\n",
    "    nafp_ir_ts_durations.append(len(ir)/fs)\n",
    "print(\"\\nTest\")\n",
    "print(f\"There are {len(nafp_ir_ts_durations)} IRs\")\n",
    "nafp_ir_ts_durations = np.array(nafp_ir_ts_durations)\n",
    "print(f\"Min duration: {nafp_ir_ts_durations.min():.4f} s\")\n",
    "print(f\"Max duration: {nafp_ir_ts_durations.max():.4f} s\")\n",
    "print(f\"Mean duration: {nafp_ir_ts_durations.mean():.4f} s\")\n",
    "print(f\"Std duration: {nafp_ir_ts_durations.std():.4f} s\")\n",
    "print(f\"Median duration: {np.median(nafp_ir_ts_durations):.4f} s\")\n",
    "print(f\"Total duration: {nafp_ir_ts_durations.sum()/60:.4f} m\")\n",
    "\n",
    "nafp_long_ir_ts_durs = [dur for dur in nafp_ir_ts_durations if dur > T]\n",
    "print(f\"{len(nafp_long_ir_ts_durs)} files are longer than {T} s\")\n",
    "print(f\"Total duration of these files: {sum(nafp_long_ir_ts_durs)/60:.4f} m\")\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, figsize=(18, 5), constrained_layout=True, sharex=True)\n",
    "fig.suptitle(\"NAFP IR Splits' Duration Histograms\")\n",
    "\n",
    "x_max = max(max(nafp_ir_tr_durations), max(nafp_ir_ts_durations))\n",
    "\n",
    "ax[0].hist(nafp_ir_tr_durations, bins=np.arange(0, x_max+0.5, 0.25))\n",
    "ax[0].set_title(\"Train Set\")\n",
    "\n",
    "ax[1].hist(nafp_ir_ts_durations, bins=np.arange(0, x_max+0.5, 0.25))\n",
    "ax[1].set_title(\"Test Set\")\n",
    "ax[1].set_xlabel(\"Duration (s)\")\n",
    "\n",
    "for _x in ax:\n",
    "    _x.set_xticks(np.arange(0, x_max+0.5, 0.5))\n",
    "    _x.set_xlim([0-0.25,x_max+0.5])\n",
    "    _x.set_ylabel(\"Count\")\n",
    "    _x.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listen to the rooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39 23.01075 ../../datasets/neural-audio-fp-dataset/aug/ir/ts/terrys_warehouse_stereo.wav\n",
      "67 20.28 ../../datasets/neural-audio-fp-dataset/aug/ir/ts/r1_ortf-48k.wav\n"
     ]
    }
   ],
   "source": [
    "for i,ir_dur in enumerate(nafp_ir_ts_durations):\n",
    "    if ir_dur > 10.0:\n",
    "        print(i,ir_dur,nafp_ir_ts_paths[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipd.display(ipd.Audio(nafp_ir_ts_paths[0], rate=fs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NAFP IR File Type Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "440\n"
     ]
    }
   ],
   "source": [
    "nafp_ir_paths = sorted(glob(os.path.join(nafp_ir_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "print(len(nafp_ir_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "197\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['18a_smoking_room.wav',\n",
       " '1_c414_r.wav',\n",
       " '1_nt1_l.wav',\n",
       " '1a_marble_hall.wav',\n",
       " '3_c414_l.wav',\n",
       " '3_nt1_l.wav',\n",
       " '3_nt1_r.wav',\n",
       " '3a_hats_cloaks_the_lord.wav',\n",
       " '4_c414_r.wav',\n",
       " '4a_hats_cloaks_visitors.wav',\n",
       " '5_c414_r.wav',\n",
       " '5_nt1_r.wav',\n",
       " 'Altec_639.wav',\n",
       " 'Altec_670A.wav',\n",
       " 'Altec_670B.wav',\n",
       " 'American_R331.wav',\n",
       " 'Amperite_RA.wav',\n",
       " 'BBCmarconi_B.wav',\n",
       " 'BeO_BM2.wav',\n",
       " 'BeO_BM6.wav',\n",
       " 'Beomic_1000.wav',\n",
       " 'Beyer_M260.wav',\n",
       " 'Beyer_M360.wav',\n",
       " 'BnO_H9i.wav',\n",
       " 'Coles_4038.wav',\n",
       " 'Cozy_usbmic.wav',\n",
       " 'Doremi_351.wav',\n",
       " 'EMI_ribbon.wav',\n",
       " 'EV_RE20_Flat.wav',\n",
       " 'EV_RE20_HPF.wav',\n",
       " 'Fairpods2.wav',\n",
       " 'FilmIndustries_M8.wav',\n",
       " 'GEC_2373_Ribbon.wav',\n",
       " 'GEC_bigdynamic.wav',\n",
       " 'GNote2.wav',\n",
       " 'Grampian_GR2.wav',\n",
       " 'IR_AKGD12.wav',\n",
       " 'IR_AKG_FaultyD12.wav',\n",
       " 'IR_Astatic77.wav',\n",
       " 'IR_BeyerM500Stock.wav',\n",
       " 'IR_FosterDynamicDF1.wav',\n",
       " 'IR_Framez.wav',\n",
       " 'IR_GaumontKalee.wav',\n",
       " 'IR_Lomo52A5M.wav',\n",
       " 'IR_Meazzi.wav',\n",
       " 'IR_MelodiumRM6.wav',\n",
       " 'IR_OktavaMK18Silver.wav',\n",
       " 'IR_OktavaMK18_Overload.wav',\n",
       " 'IR_OktavaML16.wav',\n",
       " 'IR_RCAKU3a.wav',\n",
       " 'IR_ResloCR600.wav',\n",
       " 'IR_ResloDynamic.wav',\n",
       " 'IR_ResloURA.wav',\n",
       " 'IR_STC4033_Cardioid.wav',\n",
       " 'IR_STC4033_Pressure.wav',\n",
       " 'IR_STC4033_Ribbon.wav',\n",
       " 'IR_Shure510C.wav',\n",
       " 'Melodium_42B_1.wav',\n",
       " 'Melodium_Model12.wav',\n",
       " 'Mote2.wav',\n",
       " 'Oktava_ML19.wav',\n",
       " 'Pillar_usbmic.wav',\n",
       " 'QS7.wav',\n",
       " 'RCA_44BX_1.wav',\n",
       " 'RCA_44BX_2.wav',\n",
       " 'RCA_74B.wav',\n",
       " 'RCA_77DX_1.wav',\n",
       " 'RCA_77DX_2.wav',\n",
       " 'RCA_KU3a_1.wav',\n",
       " 'RCA_PB90.wav',\n",
       " 'RCA_varacoustic_fig8.wav',\n",
       " 'RS3.wav',\n",
       " 'RS7.wav',\n",
       " 'RS8.wav',\n",
       " 'Reslo_RB250.wav',\n",
       " 'Reslo_RB_RedLabel.wav',\n",
       " 'Reslo_RV.wav',\n",
       " 'Reslo_SR1.wav',\n",
       " 'Reslo_VMC2.wav',\n",
       " 'S3.wav',\n",
       " 'S7.wav',\n",
       " 'S8.wav',\n",
       " 'Samsungbundle.wav',\n",
       " 'Shure315_HPF.wav',\n",
       " 'Shure315_flat.wav',\n",
       " 'Sony_C37Fet.wav',\n",
       " 'Telefunken_M201.wav',\n",
       " 'Toshiba_TypeG.wav',\n",
       " 'Toshiba_TypeH_BK5.wav',\n",
       " 'Toshiba_TypeK_HPF.wav',\n",
       " 'Toshiba_TypeK_flat.wav',\n",
       " 'Xiaomipiston.wav',\n",
       " 'Zamsungbundle.wav',\n",
       " 'Zony pcm-a10.wav',\n",
       " 'air_binaural_aula_carolina_1_1_1_90_3.wav',\n",
       " 'air_binaural_booth_0_1_1.wav',\n",
       " 'air_binaural_lecture_1_0_2.wav',\n",
       " 'air_binaural_meeting_1_0_5.wav',\n",
       " 'air_binaural_office_1_1_3.wav',\n",
       " 'air_binaural_stairway_0_1_2_45.wav',\n",
       " 'air_binaural_stairway_1_1_1_60.wav',\n",
       " 'air_binaural_stairway_1_1_3_30.wav',\n",
       " 'air_phone_corridor_hfrp_1.wav',\n",
       " 'air_phone_meeting_hfrp_1.wav',\n",
       " 'air_phone_stairway2_hhp_1.wav',\n",
       " 'axiaomimia1.wav',\n",
       " 'concerthall_1mleft_fl.wav',\n",
       " 'concerthall_2mleft_sr.wav',\n",
       " 'concerthall_8mright_sr.wav',\n",
       " 'concertspeech2mright_sl.wav',\n",
       " 'conecerthall_4mright_fl.wav',\n",
       " 'conecrtspeech8mleft_fr.wav',\n",
       " 'eduleft_sl.wav',\n",
       " 'eduright_fr.wav',\n",
       " 'eduright_sl.wav',\n",
       " 'fakeairpod.wav',\n",
       " 'falkland_tennis_court_ortf.wav',\n",
       " 'gamsungS3.wav',\n",
       " 'hairpodpro.wav',\n",
       " 'hairpods1.wav',\n",
       " 'hearpod.wav',\n",
       " 'iphoneX.wav',\n",
       " 'iphoneX.wav',\n",
       " 'ir_church_saint-laurentius_molenbeek_bekkevoort_belgium.wav',\n",
       " 'jphone6.wav',\n",
       " 'jphoneXS.wav',\n",
       " 'left_fl.wav',\n",
       " 'left_fr.wav',\n",
       " 'left_fr.wav',\n",
       " 'left_sl.wav',\n",
       " 'left_sl.wav',\n",
       " 'left_sr.wav',\n",
       " 'left_sr.wav',\n",
       " 'lyd3_000_ortf_48k.wav',\n",
       " 'music2mright_sl.wav',\n",
       " 'music_2mleft_sl.wav',\n",
       " 'ortf_s1r1.wav',\n",
       " 'ortf_s2r2.wav',\n",
       " 'ortf_s3r3.wav',\n",
       " 'pixel3.wav',\n",
       " 'pobmarley.wav',\n",
       " 'r1_ortf-48k.wav',\n",
       " 'r1_ortf.wav',\n",
       " 'right_fl.wav',\n",
       " 'right_fr.wav',\n",
       " 'right_fr.wav',\n",
       " 'right_sl.wav',\n",
       " 'right_sl.wav',\n",
       " 'right_sr.wav',\n",
       " 'right_sr.wav',\n",
       " 'sennheisermm550x.wav',\n",
       " 'sonicast_dirak_mk2.wav',\n",
       " 'source1domefareceiver2domelabinaural.wav',\n",
       " 'source1domefareceiver3domesibinaural.wav',\n",
       " 'source1domefareceiver4domedobinaural.wav',\n",
       " 'source1domefareceiver5domemibinaural.wav',\n",
       " 'source2domelareceiver4domedobinaural.wav',\n",
       " 'source2domelareceiver5domemibinaural.wav',\n",
       " 'source3domesireceiver4domedobinaural.wav',\n",
       " 'source3domesireceiver5domemibinaural.wav',\n",
       " 'source4domedoreceiver1domefabinaural.wav',\n",
       " 'source4domedoreceiver2domelabinaural.wav',\n",
       " 'source4domedoreceiver3domesibinaural.wav',\n",
       " 'source5domemireceiver2domelabinaural.wav',\n",
       " 'source5domemireceiver3domesibinaural.wav',\n",
       " 'source5domemireceiver4domedobinaural.wav',\n",
       " 'sp1_mp1_ir_stereo_trimmed.wav',\n",
       " 'sp1_mp2_ir_stereo_trimmed.wav',\n",
       " 'sp1_mp3_ir_stereo_trimmed.wav',\n",
       " 'sp1_mp4_ir_stereo_trimmed.wav',\n",
       " 'sp1_mp5_ir_stereo_trimmed.wav',\n",
       " 'sp2_mp1_ir_stereo_trimmed.wav',\n",
       " 'sp2_mp3_ir_stereo_trimmed.wav',\n",
       " 'speech_1mleft_fr.wav',\n",
       " 'speech_2mleft_fr.wav',\n",
       " 'spokane_womans_club_ir.wav',\n",
       " 'sportscentre_ortf.wav',\n",
       " 'st_georges_close.wav',\n",
       " 'st_georges_far.wav',\n",
       " 'st_georges_far.wav',\n",
       " 'st_georges_medium.wav',\n",
       " 'stairwell_ir_stereo_trimmed.wav',\n",
       " 'stairwell_ortf.wav',\n",
       " 'terrys_typing_ortf.wav',\n",
       " 'terrys_warehouse_stereo.wav',\n",
       " 'tyndall_bruce_ortf.wav',\n",
       " 'usina_main_s1_p1.wav',\n",
       " 'usina_main_s1_p3.wav',\n",
       " 'usina_main_s1_p4.wav',\n",
       " 'usina_main_s2_p1.wav',\n",
       " 'usina_main_s2_p2.wav',\n",
       " 'usina_main_s2_p3.wav',\n",
       " 'usina_main_s2_p4.wav',\n",
       " 'usina_main_s2_p5.wav',\n",
       " 'vegabundle.wav',\n",
       " 'xiaomi_mi_airdots2.wav',\n",
       " 'xiaomimia1.wav']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# __ signifies that a RIR is convolved with a MIR\n",
    "nafp_ir_paths_filtered = sorted([path for path in nafp_ir_paths if \"__\" not in path])\n",
    "print(len(nafp_ir_paths_filtered))\n",
    "\n",
    "nafp_ir_names_filtered = sorted([os.path.basename(path) for path in nafp_ir_paths_filtered])\n",
    "nafp_ir_names_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "59\n"
     ]
    }
   ],
   "source": [
    "nafp_aachen_names = sorted(list(set(nafp_ir_names_filtered).intersection(set(aachen_ir_names))))\n",
    "print(len(nafp_aachen_names))\n",
    "\n",
    "nafp_open_air_names = sorted(list(set(nafp_ir_names_filtered).intersection(set(open_air_ir_names))))\n",
    "print(len(nafp_open_air_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    }
   ],
   "source": [
    "nafp_air_ir_names = [\n",
    "    'air_binaural_aula_carolina_1_1_1_90_3.wav',\n",
    "    'air_binaural_booth_0_1_1.wav',\n",
    "    'air_binaural_lecture_1_0_2.wav',\n",
    "    'air_binaural_meeting_1_0_5.wav',\n",
    "    'air_binaural_office_1_1_3.wav',\n",
    "    'air_binaural_stairway_0_1_2_45.wav',\n",
    "    'air_binaural_stairway_1_1_1_60.wav',\n",
    "    'air_binaural_stairway_1_1_3_30.wav',\n",
    "    'air_phone_corridor_hfrp_1.wav',\n",
    "    'air_phone_meeting_hfrp_1.wav',\n",
    "    'air_phone_stairway2_hhp_1.wav',\n",
    "]\n",
    "print(len(nafp_air_ir_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Altec_639.wav',\n",
       " 'Altec_670A.wav',\n",
       " 'Altec_670B.wav',\n",
       " 'American_R331.wav',\n",
       " 'Amperite_RA.wav',\n",
       " 'BBCmarconi_B.wav',\n",
       " 'BeO_BM2.wav',\n",
       " 'BeO_BM6.wav',\n",
       " 'Beomic_1000.wav',\n",
       " 'Beyer_M260.wav',\n",
       " 'Beyer_M360.wav',\n",
       " 'BnO_H9i.wav',\n",
       " 'Coles_4038.wav',\n",
       " 'Cozy_usbmic.wav',\n",
       " 'Doremi_351.wav',\n",
       " 'EMI_ribbon.wav',\n",
       " 'EV_RE20_Flat.wav',\n",
       " 'EV_RE20_HPF.wav',\n",
       " 'Fairpods2.wav',\n",
       " 'FilmIndustries_M8.wav',\n",
       " 'GEC_2373_Ribbon.wav',\n",
       " 'GEC_bigdynamic.wav',\n",
       " 'GNote2.wav',\n",
       " 'Grampian_GR2.wav',\n",
       " 'IR_AKGD12.wav',\n",
       " 'IR_AKG_FaultyD12.wav',\n",
       " 'IR_Astatic77.wav',\n",
       " 'IR_BeyerM500Stock.wav',\n",
       " 'IR_FosterDynamicDF1.wav',\n",
       " 'IR_Framez.wav',\n",
       " 'IR_GaumontKalee.wav',\n",
       " 'IR_Lomo52A5M.wav',\n",
       " 'IR_Meazzi.wav',\n",
       " 'IR_MelodiumRM6.wav',\n",
       " 'IR_OktavaMK18Silver.wav',\n",
       " 'IR_OktavaMK18_Overload.wav',\n",
       " 'IR_OktavaML16.wav',\n",
       " 'IR_RCAKU3a.wav',\n",
       " 'IR_ResloCR600.wav',\n",
       " 'IR_ResloDynamic.wav',\n",
       " 'IR_ResloURA.wav',\n",
       " 'IR_STC4033_Cardioid.wav',\n",
       " 'IR_STC4033_Pressure.wav',\n",
       " 'IR_STC4033_Ribbon.wav',\n",
       " 'IR_Shure510C.wav',\n",
       " 'Melodium_42B_1.wav',\n",
       " 'Melodium_Model12.wav',\n",
       " 'Mote2.wav',\n",
       " 'Oktava_ML19.wav',\n",
       " 'Pillar_usbmic.wav',\n",
       " 'QS7.wav',\n",
       " 'RCA_44BX_1.wav',\n",
       " 'RCA_44BX_2.wav',\n",
       " 'RCA_74B.wav',\n",
       " 'RCA_77DX_1.wav',\n",
       " 'RCA_77DX_2.wav',\n",
       " 'RCA_KU3a_1.wav',\n",
       " 'RCA_PB90.wav',\n",
       " 'RCA_varacoustic_fig8.wav',\n",
       " 'RS3.wav',\n",
       " 'RS7.wav',\n",
       " 'RS8.wav',\n",
       " 'Reslo_RB250.wav',\n",
       " 'Reslo_RB_RedLabel.wav',\n",
       " 'Reslo_RV.wav',\n",
       " 'Reslo_SR1.wav',\n",
       " 'Reslo_VMC2.wav',\n",
       " 'S3.wav',\n",
       " 'S7.wav',\n",
       " 'S8.wav',\n",
       " 'Samsungbundle.wav',\n",
       " 'Shure315_HPF.wav',\n",
       " 'Shure315_flat.wav',\n",
       " 'Sony_C37Fet.wav',\n",
       " 'Telefunken_M201.wav',\n",
       " 'Toshiba_TypeG.wav',\n",
       " 'Toshiba_TypeH_BK5.wav',\n",
       " 'Toshiba_TypeK_HPF.wav',\n",
       " 'Toshiba_TypeK_flat.wav',\n",
       " 'Xiaomipiston.wav',\n",
       " 'Zamsungbundle.wav',\n",
       " 'Zony pcm-a10.wav',\n",
       " 'axiaomimia1.wav',\n",
       " 'concerthall_1mleft_fl.wav',\n",
       " 'concerthall_2mleft_sr.wav',\n",
       " 'concerthall_8mright_sr.wav',\n",
       " 'concertspeech2mright_sl.wav',\n",
       " 'conecerthall_4mright_fl.wav',\n",
       " 'conecrtspeech8mleft_fr.wav',\n",
       " 'eduleft_sl.wav',\n",
       " 'eduright_fr.wav',\n",
       " 'eduright_sl.wav',\n",
       " 'fakeairpod.wav',\n",
       " 'gamsungS3.wav',\n",
       " 'hairpodpro.wav',\n",
       " 'hairpods1.wav',\n",
       " 'hearpod.wav',\n",
       " 'iphoneX.wav',\n",
       " 'jphone6.wav',\n",
       " 'jphoneXS.wav',\n",
       " 'left_fl.wav',\n",
       " 'left_fr.wav',\n",
       " 'left_sl.wav',\n",
       " 'left_sr.wav',\n",
       " 'music2mright_sl.wav',\n",
       " 'music_2mleft_sl.wav',\n",
       " 'pixel3.wav',\n",
       " 'pobmarley.wav',\n",
       " 'right_fl.wav',\n",
       " 'right_fr.wav',\n",
       " 'right_sl.wav',\n",
       " 'right_sr.wav',\n",
       " 'sennheisermm550x.wav',\n",
       " 'sonicast_dirak_mk2.wav',\n",
       " 'speech_1mleft_fr.wav',\n",
       " 'speech_2mleft_fr.wav',\n",
       " 'vegabundle.wav',\n",
       " 'xiaomi_mi_airdots2.wav',\n",
       " 'xiaomimia1.wav']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(list(set(nafp_ir_names_filtered).difference(nafp_open_air_names).difference(nafp_air_ir_names)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65\n",
      "65\n"
     ]
    }
   ],
   "source": [
    "vintage_microphone_names = [\n",
    "    'Altec_639.wav',\n",
    "    'Altec_670A.wav',\n",
    "    'Altec_670B.wav',\n",
    "    'American_R331.wav',\n",
    "    'Amperite_RA.wav',\n",
    "    'BBCmarconi_B.wav',\n",
    "    'BeO_BM2.wav',\n",
    "    'BeO_BM6.wav',\n",
    "    'Beomic_1000.wav',\n",
    "    'Beyer_M260.wav',\n",
    "    'Beyer_M360.wav',\n",
    "    'Coles_4038.wav',\n",
    "    'Doremi_351.wav',\n",
    "    'EMI_ribbon.wav',\n",
    "    'EV_RE20_Flat.wav',\n",
    "    'EV_RE20_HPF.wav',\n",
    "    'FilmIndustries_M8.wav',\n",
    "    'GEC_2373_Ribbon.wav',\n",
    "    'GEC_bigdynamic.wav',\n",
    "    'Grampian_GR2.wav',\n",
    "    'IR_AKGD12.wav',\n",
    "    'IR_AKG_FaultyD12.wav',\n",
    "    'IR_Astatic77.wav',\n",
    "    'IR_BeyerM500Stock.wav',\n",
    "    'IR_FosterDynamicDF1.wav',\n",
    "    'IR_Framez.wav',\n",
    "    'IR_GaumontKalee.wav',\n",
    "    'IR_Lomo52A5M.wav',\n",
    "    'IR_Meazzi.wav',\n",
    "    'IR_MelodiumRM6.wav',\n",
    "    'IR_OktavaMK18Silver.wav',\n",
    "    'IR_OktavaMK18_Overload.wav',\n",
    "    'IR_OktavaML16.wav',\n",
    "    'IR_RCAKU3a.wav',\n",
    "    'IR_ResloCR600.wav',\n",
    "    'IR_ResloDynamic.wav',\n",
    "    'IR_ResloURA.wav',\n",
    "    'IR_STC4033_Cardioid.wav',\n",
    "    'IR_STC4033_Pressure.wav',\n",
    "    'IR_STC4033_Ribbon.wav',\n",
    "    'IR_Shure510C.wav',\n",
    "    'Melodium_42B_1.wav',\n",
    "    'Melodium_Model12.wav',\n",
    "    'Oktava_ML19.wav',\n",
    "    'RCA_44BX_1.wav',\n",
    "    'RCA_44BX_2.wav',\n",
    "    'RCA_74B.wav',\n",
    "    'RCA_77DX_1.wav',\n",
    "    'RCA_77DX_2.wav',\n",
    "    'RCA_KU3a_1.wav',\n",
    "    'RCA_PB90.wav',\n",
    "    'RCA_varacoustic_fig8.wav',\n",
    "    'Reslo_RB250.wav',\n",
    "    'Reslo_RB_RedLabel.wav',\n",
    "    'Reslo_RV.wav',\n",
    "    'Reslo_SR1.wav',\n",
    "    'Reslo_VMC2.wav',\n",
    "    'Shure315_HPF.wav',\n",
    "    'Shure315_flat.wav',\n",
    "    'Sony_C37Fet.wav',\n",
    "    'Telefunken_M201.wav',\n",
    "    'Toshiba_TypeG.wav',\n",
    "    'Toshiba_TypeH_BK5.wav',\n",
    "    'Toshiba_TypeK_HPF.wav',\n",
    "    'Toshiba_TypeK_flat.wav',\n",
    "]\n",
    "print(len(vintage_microphone_names))\n",
    "\n",
    "nafp_vintage_mic_ir_paths = [ir_path for ir_path in nafp_ir_paths_filtered if os.path.basename(ir_path) in vintage_microphone_names]\n",
    "print(len(nafp_vintage_mic_ir_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "nafp_other_microphone_names = [\n",
    "    'Cozy_usbmic.wav', # usb Mic computer\n",
    "    'Pillar_usbmic.wav',\n",
    "    'Zony pcm-a10.wav', # Sony PCM-A10 good quality microphone\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "17\n"
     ]
    }
   ],
   "source": [
    "nafp_phone_microphone_names = [\n",
    "    'GNote2.wav',\n",
    "    'xiaomi_mi_airdots2.wav',\n",
    "    'xiaomimia1.wav',\n",
    "    'iphoneX.wav',\n",
    "    'jphone6.wav',\n",
    "    'jphoneXS.wav',\n",
    "    'gamsungS3.wav',\n",
    "    'S3.wav',\n",
    "    'S7.wav',\n",
    "    'S8.wav',\n",
    "    'Samsungbundle.wav',\n",
    "    'Xiaomipiston.wav',\n",
    "    'Zamsungbundle.wav',\n",
    "    'Mote2.wav', # not sure if Note2 phone\n",
    "    'axiaomimia1.wav',\n",
    "    'pixel3.wav',\n",
    "]\n",
    "print(len(nafp_phone_microphone_names))\n",
    "\n",
    "# iphoneX.wav is both in tr and ts!!!!\n",
    "nafp_phone_mic_paths = [ir_path for ir_path in nafp_ir_paths_filtered if os.path.basename(ir_path) in nafp_phone_microphone_names]\n",
    "print(len(nafp_phone_mic_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "nafp_headphone_names = [\n",
    "    'BnO_H9i.wav', # headphone\n",
    "    'Fairpods2.wav', # I guess apple airpods\n",
    "    'fakeairpod.wav',\n",
    "    'hairpodpro.wav',\n",
    "    'hairpods1.wav',\n",
    "    'hearpod.wav',\n",
    "    'sennheisermm550x.wav', # headphone\n",
    "    'sonicast_dirak_mk2.wav', # Sonicast Dirac+ MK2 headphone\n",
    "]\n",
    "print(len(nafp_headphone_names))\n",
    "\n",
    "nafp_headphone_ir_paths = [ir_path for ir_path in nafp_ir_paths_filtered if os.path.basename(ir_path) in nafp_headphone_names]\n",
    "print(len(nafp_headphone_ir_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['QS7.wav',\n",
       " 'RS3.wav',\n",
       " 'RS7.wav',\n",
       " 'RS8.wav',\n",
       " 'concerthall_1mleft_fl.wav',\n",
       " 'concerthall_2mleft_sr.wav',\n",
       " 'concerthall_8mright_sr.wav',\n",
       " 'concertspeech2mright_sl.wav',\n",
       " 'conecerthall_4mright_fl.wav',\n",
       " 'conecrtspeech8mleft_fr.wav',\n",
       " 'eduleft_sl.wav',\n",
       " 'eduright_fr.wav',\n",
       " 'eduright_sl.wav',\n",
       " 'left_fl.wav',\n",
       " 'left_fr.wav',\n",
       " 'left_sl.wav',\n",
       " 'left_sr.wav',\n",
       " 'music2mright_sl.wav',\n",
       " 'music_2mleft_sl.wav',\n",
       " 'pobmarley.wav',\n",
       " 'right_fl.wav',\n",
       " 'right_fr.wav',\n",
       " 'right_sl.wav',\n",
       " 'right_sr.wav',\n",
       " 'speech_1mleft_fr.wav',\n",
       " 'speech_2mleft_fr.wav',\n",
       " 'vegabundle.wav']"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nafp_unkown_ir_names = sorted(list(set(nafp_ir_names_filtered).difference(nafp_open_air_names).difference(nafp_air_ir_names).difference(vintage_microphone_names).difference(nafp_other_microphone_names).difference(nafp_phone_microphone_names).difference(nafp_headphone_names)))\n",
    "print(len(nafp_unkown_ir_names))\n",
    "nafp_unkown_ir_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NMFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "364\n",
      "109\n"
     ]
    }
   ],
   "source": [
    "nafp_pp_room_ir_dir = os.path.join(nafp_pp_dataset_dir, \"aug\", \"room_ir\")\n",
    "\n",
    "nafp_pp_tr_room_ir_paths = sorted(glob(os.path.join(nafp_pp_room_ir_dir, \"dev\", \"**\", \"*.wav\"), recursive=True))\n",
    "print(len(nafp_pp_tr_room_ir_paths))\n",
    "\n",
    "nafp_pp_ts_room_ir_paths = sorted(glob(os.path.join(nafp_pp_room_ir_dir, \"test\", \"**\", \"*.wav\"), recursive=True))\n",
    "print(len(nafp_pp_ts_room_ir_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nafp_pp_tr_room_ir_durations = []\n",
    "for ir_path in nafp_pp_tr_room_ir_paths:\n",
    "    ir = audio_processing.load_wav(ir_path, fs=fs)\n",
    "    nafp_pp_tr_room_ir_durations.append(len(ir)/fs)\n",
    "nafp_pp_tr_room_ir_durations = np.array(nafp_pp_tr_room_ir_durations)\n",
    "\n",
    "nafp_pp_ts_room_ir_durations = []\n",
    "for ir_path in nafp_pp_ts_room_ir_paths:\n",
    "    ir = audio_processing.load_wav(ir_path, fs=fs)\n",
    "    nafp_pp_ts_room_ir_durations.append(len(ir)/fs)\n",
    "nafp_pp_ts_room_ir_durations = np.array(nafp_pp_ts_room_ir_durations)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, figsize=(18, 10), constrained_layout=True, sharex=True)\n",
    "fig.suptitle(\"NAFP_PP Room IR Splits Duration Histograms\")\n",
    "\n",
    "x_max = max(max(nafp_pp_tr_room_ir_durations), max(nafp_pp_ts_room_ir_durations))\n",
    "\n",
    "ax[0].hist(nafp_pp_tr_room_ir_durations, bins=np.arange(0, x_max+0.5, 0.25))\n",
    "ax[0].set_title(\"Train Set\")\n",
    "\n",
    "ax[1].hist(nafp_pp_ts_room_ir_durations, bins=np.arange(0, x_max+0.5, 0.25))\n",
    "ax[1].set_title(\"Test Set\")\n",
    "ax[1].set_xlabel(\"Duration (s)\")\n",
    "\n",
    "for _x in ax:\n",
    "    _x.set_xticks(np.arange(0, x_max+0.5, 0.5))\n",
    "    _x.set_xlim([0-0.25,x_max+0.5])\n",
    "    _x.set_ylabel(\"Count\")\n",
    "    _x.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MIT dev 216\n",
      "MIT test 54\n",
      "OPENAIR dev 105\n",
      "OPENAIR test 38\n",
      "AachenIR dev 43\n",
      "AachenIR test 17\n"
     ]
    }
   ],
   "source": [
    "for dataset_name in [\"MIT\", 'OPENAIR', 'AachenIR']:\n",
    "    for split_name in [\"dev\", \"test\"]:\n",
    "        split_dir = os.path.join(nafp_pp_room_ir_dir, split_name, dataset_name)\n",
    "        split_paths = sorted(glob(os.path.join(split_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "        print(f\"{dataset_name} {split_name} {len(split_paths)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=3, ncols=2, figsize=(18, 10), constrained_layout=True, sharex=True)\n",
    "fig.suptitle(\"NAFP_PP Room IR Splits Duration Histograms\")\n",
    "\n",
    "for row,dataset_name in zip(axs, [\"MIT\", 'OPENAIR', 'AachenIR']):\n",
    "    for ax,split_name in zip(row, [\"dev\", \"test\"]):\n",
    "        split_dir = os.path.join(nafp_pp_room_ir_dir, split_name, dataset_name)\n",
    "        split_paths = sorted(glob(os.path.join(split_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "        split_durations = []\n",
    "        for ir_path in split_paths:\n",
    "            ir = audio_processing.load_wav(ir_path, fs=fs)\n",
    "            split_durations.append(len(ir)/fs)\n",
    "        split_durations = np.array(split_durations)\n",
    "        x_max = max(split_durations)\n",
    "        ax.hist(split_durations, bins=np.arange(0, x_max+0.25, 0.25))\n",
    "        ax.set_title(f\"{dataset_name} {split_name} Set\")\n",
    "        ax.set_xticks(np.arange(0, 24, 1))\n",
    "        ax.set_xlim([0,23])\n",
    "        ax.set_ylabel(\"Count\")\n",
    "        ax.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split IRs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import shutil\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Aachen IR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "aachen_bin_ir_rooms_tr = ['meeting', 'lecture', 'stairway', 'office']\n",
    "aachen_bin_ir_rooms_ts = ['aula_carolina', 'booth']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 270 IRs\n",
      "{\n",
      "    \"1\": 52,\n",
      "    \"2\": 72,\n",
      "    \"0\": 65,\n",
      "    \"4\": 17,\n",
      "    \"3\": 35,\n",
      "    \"5\": 10,\n",
      "    \"7\": 14,\n",
      "    \"6\": 5\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "mit_ir_durations = []\n",
    "mit_labels = []\n",
    "for ir_path in mit_ir_paths:\n",
    "    ir_len = audio_processing.check_wav_file(ir_path, fs=fs)\n",
    "    ir_dur = ir_len/fs\n",
    "    mit_ir_durations.append(ir_dur)\n",
    "    if ir_dur < 0.25:\n",
    "        mit_labels.append(\"0\")\n",
    "    elif ir_dur < 0.5:\n",
    "        mit_labels.append(\"1\")\n",
    "    elif ir_dur < 0.75:\n",
    "        mit_labels.append(\"2\")\n",
    "    elif ir_dur < 1.0:\n",
    "        mit_labels.append(\"3\")\n",
    "    elif ir_dur < 1.25:\n",
    "        mit_labels.append(\"4\")\n",
    "    elif ir_dur < 1.5:\n",
    "        mit_labels.append(\"5\")\n",
    "    elif ir_dur < 1.75:\n",
    "        mit_labels.append(\"6\")\n",
    "    else:\n",
    "        mit_labels.append(\"7\")\n",
    "\n",
    "# print(\"Train\")\n",
    "print(f\"There are {len(mit_ir_durations)} IRs\")\n",
    "mit_ir_durations = np.array(mit_ir_durations)\n",
    "\n",
    "counter = Counter(mit_labels)\n",
    "print(json.dumps(counter, indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "216\n",
      "54\n"
     ]
    }
   ],
   "source": [
    "mit_train_paths, mit_test_paths = train_test_split(mit_ir_paths, test_size=0.20, random_state=27, stratify=mit_labels)\n",
    "print(len(mit_train_paths))\n",
    "print(len(mit_test_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(mit_train_paths).intersection(set(mit_test_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mit_ir_tr_durations = []\n",
    "for ir_path in mit_train_paths:\n",
    "    audio_len = audio_processing.check_wav_file(ir_path, fs=fs)\n",
    "    mit_ir_tr_durations.append(audio_len/fs)\n",
    "\n",
    "mit_ir_ts_durations = []\n",
    "for ir_path in mit_test_paths:\n",
    "    audio_len = audio_processing.check_wav_file(ir_path, fs=fs)\n",
    "    mit_ir_ts_durations.append(audio_len/fs)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, figsize=(18, 5), constrained_layout=True, sharex=True)\n",
    "fig.suptitle(\"MIT IR Splits' Duration Histograms\")\n",
    "\n",
    "x_max = max(max(mit_ir_tr_durations), max(mit_ir_ts_durations))\n",
    "\n",
    "ax[0].hist(mit_ir_tr_durations, bins=np.arange(0, x_max+0.1, 0.25))\n",
    "ax[0].set_title(\"Train Set\")\n",
    "\n",
    "ax[1].hist(mit_ir_ts_durations, bins=np.arange(0, x_max+0.1, 0.25))\n",
    "ax[1].set_title(\"Test Set\")\n",
    "ax[1].set_xlabel(\"Duration (s)\")\n",
    "\n",
    "for _x in ax:\n",
    "    _x.set_xticks(np.arange(0, x_max+0.05, 0.25))\n",
    "    _x.set_xlim([0-0.05,x_max+0.05])\n",
    "    _x.set_ylabel(\"Count\")\n",
    "    _x.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "nafp_pp_room_ir_dir = os.path.join(nafp_pp_dataset_dir, \"aug\", \"room_ir\")\n",
    "\n",
    "new_ir_tr_dir = os.path.join(nafp_pp_room_ir_dir, 'dev', 'MIT')\n",
    "new_ir_ts_dir = os.path.join(nafp_pp_room_ir_dir, 'test', 'MIT')\n",
    "\n",
    "os.makedirs(new_ir_tr_dir, exist_ok=True)\n",
    "os.makedirs(new_ir_ts_dir, exist_ok=True)\n",
    "\n",
    "for ir_path in mit_train_paths:\n",
    "    shutil.copy(ir_path, new_ir_tr_dir)\n",
    "\n",
    "for ir_path in mit_test_paths:\n",
    "    shutil.copy(ir_path, new_ir_ts_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OpenAIR Split by Rooms Stratified by Room duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elveden-hall-suffolk-england 7.94 E\n",
      "falkland-palace-royal-tennis-court 4.80 B\n",
      "gill-heads-mine 5.00 C\n",
      "hamilton-mausoleum 15.00 E\n",
      "innocent-railway-tunnel 5.00 C\n",
      "koli-national-park-summer 5.00 C\n",
      "koli-national-park-winter 5.00 C\n",
      "lady-chapel-st-albans-cathedral 6.00 D\n",
      "maes-howe 1.00 A\n",
      "r1-nuclear-reactor-hall 19.94 F\n",
      "saint-lawrence-church-molenbeek-wersbeek-belgium 1.10 A\n",
      "shrine-and-parish-church-all-saints-north-street-_ 15.00 E\n",
      "spokane-womans-club 6.00 D\n",
      "sports-centre-university-york 9.80 E\n",
      "spring-lane-building-university-york 3.89 B\n",
      "st-andrews-church 2.00 A\n",
      "st-margarets-church-ncem-5-piece-band-spatial-measurements 2.00 A\n",
      "st-matthews-church-walsall 3.00 B\n",
      "st-patricks-church-patrington 6.00 D\n",
      "stairway-university-york 3.00 B\n",
      "terrys-factory-warehouse 22.67 F\n",
      "terrys-typing-room 5.80 D\n",
      "trollers-gill 5.00 C\n",
      "tyndall-bruce-monument 4.80 B\n",
      "usina-del-arte-symphony-hall 2.90 A\n",
      "waveguide-web-example-audio 5.00 C\n",
      "wheldrake-wood 2.00 A\n",
      "york-minster 10.00 E\n"
     ]
    }
   ],
   "source": [
    "open_air_8k_room_durations = []\n",
    "open_air_8k_labels = []\n",
    "for room in open_air_8k_rooms:\n",
    "    room_dir = os.path.join(open_air_8k_dir, room)\n",
    "    room_paths = sorted(glob(os.path.join(room_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "    _durations = []\n",
    "    for _path in room_paths:\n",
    "        audio_len = audio_processing.check_wav_file(_path, fs=fs)\n",
    "        _durations.append(audio_len/fs)\n",
    "    min_duration = np.min(_durations).astype(float)\n",
    "    open_air_8k_room_durations.append(min_duration)\n",
    "    if min_duration < 3:\n",
    "        open_air_8k_labels.append(\"A\")\n",
    "    elif min_duration < 5:\n",
    "        open_air_8k_labels.append(\"B\")\n",
    "    elif min_duration < 5.5:\n",
    "        open_air_8k_labels.append(\"C\")\n",
    "    elif min_duration < 7:\n",
    "        open_air_8k_labels.append(\"D\")\n",
    "    elif min_duration < 16:\n",
    "        open_air_8k_labels.append(\"E\")\n",
    "    else:\n",
    "        open_air_8k_labels.append(\"F\")\n",
    "\n",
    "for room, duration, label in zip(open_air_8k_rooms, open_air_8k_room_durations, open_air_8k_labels):\n",
    "    print(f\"{room} {duration:.2f} {label}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"E\": 5,\n",
      "    \"B\": 5,\n",
      "    \"C\": 6,\n",
      "    \"D\": 4,\n",
      "    \"A\": 6,\n",
      "    \"F\": 2\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "counter = Counter(open_air_8k_labels)\n",
    "print(json.dumps(counter, indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n",
      "7\n",
      "105\n",
      "38\n"
     ]
    }
   ],
   "source": [
    "air_train_rooms, air_test_rooms = train_test_split(open_air_8k_rooms, test_size=0.25, random_state=27, stratify=open_air_8k_labels)\n",
    "assert set(air_train_rooms).intersection(set(air_test_rooms)) == set()\n",
    "print(len(air_train_rooms))\n",
    "print(len(air_test_rooms))\n",
    "\n",
    "air_ir_train_paths = []\n",
    "for room in air_train_rooms:\n",
    "    room_dir = os.path.join(open_air_8k_dir, room)\n",
    "    room_paths = sorted(glob(os.path.join(room_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "    air_ir_train_paths.extend(room_paths)\n",
    "print(len(air_ir_train_paths))\n",
    "\n",
    "air_ir_test_paths = []\n",
    "for room in air_test_rooms:\n",
    "    room_dir = os.path.join(open_air_8k_dir, room)\n",
    "    room_paths = sorted(glob(os.path.join(room_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "    air_ir_test_paths.extend(room_paths)\n",
    "print(len(air_ir_test_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "air_tr_room_durations = []\n",
    "for room in air_train_rooms:\n",
    "    room_dir = os.path.join(open_air_8k_dir, room)\n",
    "    room_paths = sorted(glob(os.path.join(room_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "    _durations = []\n",
    "    for _path in room_paths:\n",
    "        audio_len = audio_processing.check_wav_file(_path, fs=fs)\n",
    "        _durations.append(audio_len/fs)\n",
    "    dur = np.min(_durations)\n",
    "    air_tr_room_durations.append(dur)\n",
    "\n",
    "air_ts_room_durations = []\n",
    "for room in air_test_rooms:\n",
    "    room_dir = os.path.join(open_air_8k_dir, room)\n",
    "    room_paths = sorted(glob(os.path.join(room_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "    _durations = []\n",
    "    for _path in room_paths:\n",
    "        audio_len = audio_processing.check_wav_file(_path, fs=fs)\n",
    "        _durations.append(audio_len/fs)\n",
    "    dur = np.min(_durations)\n",
    "    air_ts_room_durations.append(dur)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, figsize=(18, 5), constrained_layout=True, sharex=True)\n",
    "fig.suptitle(\"OpenAIR Room IR Splits' Duration Histograms\")\n",
    "\n",
    "x_max = max(max(air_tr_room_durations), max(air_ts_room_durations))\n",
    "\n",
    "ax[0].hist(air_tr_room_durations, bins=np.arange(0, x_max+0.5, 0.5))\n",
    "ax[0].set_title(\"Train Set\")\n",
    "\n",
    "ax[1].hist(air_ts_room_durations, bins=np.arange(0, x_max+0.5, 0.5))\n",
    "ax[1].set_title(\"Test Set\")\n",
    "ax[1].set_xlabel(\"Duration (s)\")\n",
    "\n",
    "for _x in ax:\n",
    "    _x.set_xticks(np.arange(0, x_max+0.5, 0.5))\n",
    "    _x.set_xlim([0-0.5,x_max+0.5])\n",
    "    _x.set_ylabel(\"Count\")\n",
    "    _x.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lady-chapel-st-albans-cathedral 3\n",
      "spokane-womans-club 2\n",
      "falkland-palace-royal-tennis-court 1\n",
      "stairway-university-york 2\n",
      "gill-heads-mine 4\n",
      "york-minster 2\n",
      "koli-national-park-winter 8\n",
      "terrys-factory-warehouse 1\n",
      "st-patricks-church-patrington 6\n",
      "waveguide-web-example-audio 1\n",
      "saint-lawrence-church-molenbeek-wersbeek-belgium 2\n",
      "st-andrews-church 2\n",
      "hamilton-mausoleum 2\n",
      "st-margarets-church-ncem-5-piece-band-spatial-measurements 20\n",
      "shrine-and-parish-church-all-saints-north-street-_ 6\n",
      "tyndall-bruce-monument 1\n",
      "trollers-gill 6\n",
      "elveden-hall-suffolk-england 8\n",
      "usina-del-arte-symphony-hall 8\n",
      "st-matthews-church-walsall 6\n",
      "innocent-railway-tunnel 14\n"
     ]
    }
   ],
   "source": [
    "for room in air_train_rooms:\n",
    "    room_dir = os.path.join(open_air_8k_dir, room)\n",
    "    room_paths = sorted(glob(os.path.join(room_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "    print(room, len(room_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spring-lane-building-university-york 20\n",
      "r1-nuclear-reactor-hall 1\n",
      "koli-national-park-summer 8\n",
      "sports-centre-university-york 2\n",
      "terrys-typing-room 1\n",
      "wheldrake-wood 4\n",
      "maes-howe 2\n"
     ]
    }
   ],
   "source": [
    "for room in air_test_rooms:\n",
    "    room_dir = os.path.join(open_air_8k_dir, room)\n",
    "    room_paths = sorted(glob(os.path.join(room_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "    print(room, len(room_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "air_ir_tr_durations = []\n",
    "for ir_path in air_ir_train_paths:\n",
    "    audio_len = audio_processing.check_wav_file(ir_path, fs=fs)\n",
    "    air_ir_tr_durations.append(audio_len/fs)\n",
    "air_ir_tr_durations = np.array(air_ir_tr_durations)\n",
    "\n",
    "air_ir_ts_durations = []\n",
    "for ir_path in air_ir_test_paths:\n",
    "    audio_len = audio_processing.check_wav_file(ir_path, fs=fs)\n",
    "    air_ir_ts_durations.append(audio_len/fs)\n",
    "air_ir_ts_durations = np.array(air_ir_ts_durations)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, figsize=(18, 5), constrained_layout=True, sharex=True)\n",
    "fig.suptitle(\"OpenAIR IR Splits' Duration Histograms\")\n",
    "\n",
    "x_max = max(max(air_ir_tr_durations), max(air_ir_ts_durations))\n",
    "\n",
    "ax[0].hist(air_ir_tr_durations, bins=np.arange(0, x_max+0.25, 0.25))\n",
    "ax[0].set_title(\"Train Set\")\n",
    "ax[0].set_yticks(np.arange(0, 36, 5))\n",
    "\n",
    "ax[1].hist(air_ir_ts_durations, bins=np.arange(0, x_max+0.25, 0.25))\n",
    "ax[1].set_title(\"Test Set\")\n",
    "ax[1].set_xlabel(\"Duration (s)\")\n",
    "ax[1].set_yticks(np.arange(0, 22, 3))\n",
    "\n",
    "for _x in ax:\n",
    "    _x.set_xticks(np.arange(0, x_max+0.25, 0.5))\n",
    "    _x.set_xlim([0,x_max+0.25])\n",
    "    _x.set_ylabel(\"Count\")\n",
    "    _x.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "nafp_pp_room_ir_dir = os.path.join(nafp_pp_dataset_dir, \"aug\", \"room_ir\")\n",
    "\n",
    "new_ir_tr_dir = os.path.join(nafp_pp_room_ir_dir, 'dev', 'OPENAIR')\n",
    "new_ir_ts_dir = os.path.join(nafp_pp_room_ir_dir, 'test', 'OPENAIR')\n",
    "\n",
    "os.makedirs(new_ir_tr_dir, exist_ok=True)\n",
    "os.makedirs(new_ir_ts_dir, exist_ok=True)\n",
    "\n",
    "for ir_path in air_ir_train_paths:\n",
    "    structure = os.path.sep.join(ir_path.split(os.path.sep)[-3:-1])\n",
    "    new_dir = os.path.join(new_ir_tr_dir, structure)\n",
    "    os.makedirs(new_dir, exist_ok=True)\n",
    "    shutil.copy(ir_path, new_dir)\n",
    "\n",
    "for ir_path in air_ir_test_paths:\n",
    "    structure = os.path.sep.join(ir_path.split(os.path.sep)[-3:-1])\n",
    "    new_dir = os.path.join(new_ir_ts_dir, structure)\n",
    "    os.makedirs(new_dir, exist_ok=True)\n",
    "    shutil.copy(ir_path, new_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NAFP IR New Splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "432\n",
      "There are 432 IRs\n",
      "77 IRs are shorter than 0.5 s\n",
      "Min duration: 0.0015 s\n",
      "Max duration: 23.0108 s\n",
      "Mean duration: 2.0684 s\n",
      "Std duration: 2.2052 s\n",
      "Median duration: 1.7385 s\n",
      "Total duration: 14.8924 m\n"
     ]
    },
    {
     "data": {
      "image/png": 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973+Pbt26xbBhw+L5559v0uetqKiI0047LXbbbbfYfvvta9yvkGvwi+qasdDr8KWXXooNNtggSkpK4gc/+EHceeed0bdv39R9izV/9clYjO/jW2+9NZ5//vm44IIL6rR/oeexvvkKPYdDhgyJG264IR588MG44oorYu7cufGNb3wjli1blrp/oeevvvmKsQbffPPNuOKKK2LrrbeOKVOmxMknnxw/+tGP4i9/+UuNjynkMaUh+Yp1PImIuOuuu2Lx4sUxduzYGvcp5vGkLvkKvQ4nTJgQRxxxRGy77bbRsmXL2GmnneK0006Lo48+usbHFPrnmoZkLOQ63HDDDWPo0KHxy1/+Mt57770oLy+Pm266KZ5++umYP39+6mMKOYcNyVfM7+PK78X6fJ825P1DIfNF1P+YVAzFfH2si2K95/vkk0/izDPPjCOPPDLat2+fuk8h12BDM0YUfh3ed999scEGG0Tr1q3j0ksvjalTp0bHjh1T9y3WHNYnY0Rh5/Ciiy6KFi1axI9+9KM67V/oOaxvvojCr8FRo0bFX//615g2bVpcdNFF8dhjj8U+++wT5eXlqfsXeg7rmy+isHO4cOHCWL58eVx44YUxatSoeOihh+Kggw6Kgw8+OB577LEaH1eo40lD8xXrePKXv/wlNtxww1ovO1js40ldMkYUdh1efvnl0bdv3+jatWu0atUqRo0aFWVlZfHNb36zxscU+meahmQs1DqsLJTOOuus+Pjjj+PTTz+Niy66KP773//W+F4gonBz2NB8TTV/ab8Dfv/996NVq1Zr/YetdXUplfvU9TGFzhjRsONQQ/I1RFO+HrZo1KP50hs6dGi1s5123XXX2G677eKqq66KX/7yl0VM9uXRp0+f6NOnT9XtXXfdNd5444249NJL48Ybb2yy5y0tLY2XX365Udf4b2p1zVjoddinT5+YNWtWLFmyJO64444YM2ZMPPbYYzWWdcVQn4yFnr933303Tj311Jg6dWqDP6S5KTUkX6Hn8PNnBe2www4xZMiQ6NGjR/ztb3+r0/9kbGr1zVeMY0lFRUUMGjQofvOb30RExE477RQvv/xyXHnllTFmzJgmec76aEi+Yh1PIiKuvfba2Geffer8mT2FVpd8hV6Hf/vb3+Lmm2+OW265Jfr161f1+X1dunTJxBqMaFjGQq/DG2+8MU444YTYcssto3nz5rHzzjvHkUceGc8991zen6sh6puvmN/HX1VZP2Z+GRTjOL1mzZo47LDDIkmSuOKKK5rkORqrPhkLvQ4rPxf2ww8/jGuuuSYOO+yw+Ne//tVkn1/bEPXNWKg5fO655+L3v/99PP/887VezaBYGpqv0GvwiCOOqPp7//79Y4cddojevXvH9OnT41vf+lben6++GpKvkHNYUVEREREHHHBAnH766RHxf1cteOqpp+LKK6+MPfbYI6/PV18NzVes3yFed911cfTRR2fy9w+V6pqxkOvw8ssvj2eeeSbuueee6NGjRzz++ONRWloaXbp0qfMVnJpaQzIWah22bNkyJk+eHN/97ndjk002iebNm8fw4cNjn332iSRJ8vY8hc7XVPP3Vfo9dV00xXHyyzCHzkysh44dO0bz5s1jwYIF1bYvWLAgOnfunPqYzp0712v/Quf7osr/tf7666/nPV9D1DR/7du3jzZt2hQp1brtsssuTTqH48aNi/vuuy8effTR6Nq1a637FnINfl59Mn5RU6/DVq1axde+9rUYOHBgXHDBBTFgwID4/e9/n7pvseavPhm/qKnn77nnnouFCxfGzjvvHC1atIgWLVrEY489Fn/4wx+iRYsWqf8Lp5Dz2JB8X1To18KNNtoottlmmxqfr1jrsNK68n1RIeZviy22WKtc32677Wq9REwhjykNyZemqY8nERFvv/12PPzww3HiiSfWul+x1mFd831RU6/Dn/70p1Vn/vXv3z+OPfbYOP3002s9I7rQP9c0JGOaplyHvXv3jsceeyyWL18e7777bjz77LOxZs2a2GqrrVL3L/Qc1jdfmkJ8H0dE1fdifb5P8/H+oSnzpanvMakQin2crq+mfn2sLOnefvvtmDp1aq1n/BVyDTY0Y5qmXoft2rWLr33ta/H1r389rr322mjRokVce+21qfsWaw7rkzFNU83hE088EQsXLozu3btXvRd4++2348c//nH07Nkz9TGFnMOG5EtT6NfCrbbaKjp27Fjj8xVrHdY1X5qmnMOOHTtGixYt8vZ+Jd9z2NB8X1SI931PPPFEzJkzZ53vBYq5BuuaMU1TrcNVq1bF2WefHZMmTYrRo0fHDjvsEOPGjYvDDz88Lr744hofV8ifaRqa8Yuach0OHDgwZs2aFYsXL4758+fHgw8+GB999FGt7wUKOYcNyfdF+Zi/mn4H3Llz5/j0009j8eLF1fZfV5dSuU9dH1PojGkachyqS76GaMrXQ2ViPbRq1SoGDhwY06ZNq9pWUVER06ZNq/GzzIYOHVpt/4iIqVOn1vrZZ4XM90Xl5eXx0ksvxRZbbJH3fA1RyPnLp1mzZjXJHCZJEuPGjYs777wzHnnkkejVq9c6H1PoOWxIxi8q9DqsqKioutb4F2VlDdaW8Yuaev6+9a1vxUsvvRSzZs2q+jNo0KA4+uijY9asWdG8efO1HlPIeWxIvi8q9Bpcvnx5vPHGGzU+X7HX4bryfVEh5m+33XaLOXPmVNv2n//8J3r06FHjYwo5jw3Jl6apjiefd/3118fmm28e++23X637FWsd1jXfFzX1Oly5cmU0a1b9R+nmzZtX/U/vNIWew4ZkTFOIddiuXbvYYost4uOPP44pU6bEAQcckLpfsdZhXfOlKcT8RUT06tUrOnfuXG1+li5dGv/6179qnJ98vH9oynxp6ntMKoRiH6frqylfHytLutdeey0efvjh2HTTTWvdv5BrsKEZ0xR6Hdb2XqAYc5imPu9XIppuDo899th48cUXq70X6NKlS/z0pz+NKVOmpD6mkHPYkHxpCr0G//vf/8ZHH31U4/MVex2uK1+appzDVq1axeDBgzP7fqWh+b6oEO/7rr322hg4cOA6P5O1mGuwrhnTNNU6XLNmTaxZsybT71camvGLCrEOO3ToEJtttlm89tprMXPmzFrfCxTj58L65Puixszfun4HPHDgwGjZsmW1+ZgzZ0688847Nc5Hvt4zNGXGNA05DtUlX0M06ethQr3ceuutSUlJSXLDDTcks2fPTr7//e8nG220UfL+++8nSZIkxx57bDJhwoSq/Z988smkRYsWycUXX5y88soryXnnnZe0bNkyeemllzKR7/zzz0+mTJmSvPHGG8lzzz2XHHHEEUnr1q2Tf//7302Sb9myZckLL7yQvPDCC0lEJJMmTUpeeOGF5O23306SJEkmTJiQHHvssVX7v/nmm0nbtm2Tn/70p8krr7ySlJWVJc2bN08efPDBJsnXkIyXXnppctdddyWvvfZa8tJLLyWnnnpq0qxZs+Thhx/Oe7aTTz456dChQzJ9+vRk/vz5VX9WrlxZtU+x12BDMhZyHU6YMCF57LHHkrlz5yYvvvhiMmHChCSXyyUPPfRQarZCz19DMhb6+zjNHnvskZx66qlVt7Mwj/XJV+g5/PGPf5xMnz49mTt3bvLkk08mw4cPTzp27JgsXLgwNV+h56+++YqxBp999tmkRYsWya9//evktddeS26++eakbdu2yU033VS1TzGPKQ3JV8jjSaXy8vKke/fuyZlnnrnWfcVeh/XNV+h1OGbMmGTLLbdM7rvvvmTu3LnJ5MmTk44dOyZnnHFG1T7F/rmmIRkLvQ4ffPDB5IEHHkjefPPN5KGHHkoGDBiQDBkyJPn0009T8xV6Duubr6nnb10/p1544YXJRhttlNx9993Jiy++mBxwwAFJr169klWrVlWNsddeeyWXX3551e11vX8odr51HZPynfGjjz5KXnjhheT+++9PIiK59dZbkxdeeCGZP39+1RhN+frYFPny+fpYW75PP/002X///ZOuXbsms2bNqvZeYPXq1VVjNOUabKqM+VyHteVbvnx5ctZZZyVPP/108tZbbyUzZ85Mjj/++KSkpCR5+eWXMzGHDc1YqDlM06NHj+TSSy+ttq2Yr4UNyVfI18Jly5YlP/nJT5Knn346mTt3bvLwww8nO++8c7L11lsnn3zySY0ZCzWHDc1X6OPJ5MmTk5YtWyZXX3118tprryWXX3550rx58+SJJ56oGqOYx5OG5CvU8aTSkiVLkrZt2yZXXHFF6hjFfC1saMZCvhbuscceSb9+/ZJHH300efPNN5Prr78+ad26dfKnP/2paoymfs/XFBkLuQ7/9re/JY8++mjyxhtvJHfddVfSo0eP5OCDD642RjG/jxuSL5/zV5ffAf/gBz9IunfvnjzyyCPJzJkzk6FDhyZDhw6tNk6fPn2SyZMnV92uy3uGYmas63EoX/nmz5+fvPDCC8k111yTRETy+OOPJy+88ELy0UcfVe3T1K+HlZSJDXD55Zcn3bt3T1q1apXssssuyTPPPFN13x577JGMGTOm2v5/+9vfkm222SZp1apV0q9fv+T+++/PTL7TTjutat9OnTol++67b/L88883WbZHH300iYi1/lRmGjNmTLLHHnus9Zgdd9wxadWqVbLVVlsl119/fZPla0jGiy66KOndu3fSunXrZJNNNkmGDRuWPPLII02SLS1XRFSbk2KvwYZkLOQ6POGEE5IePXokrVq1SjbbbLPkW9/6VlVJl5YtSQr/PVzfjIX+Pk7zxbIuC/NYn3yFnsPDDz882WKLLZJWrVolW265ZXL44Ycnr7/+eo35kqSw81fffMVag/fee2+y/fbbJyUlJcm2226bXH311dXuL/Yxpb75Cnk8qTRlypQkIpI5c+asdV+x12F98xV6HS5dujQ59dRTk+7duyetW7dOttpqq+RnP/tZtV9GF3sNNiRjodfhbbfdlmy11VZJq1atks6dOyelpaXJ4sWLa8yXJIWdw/rma+r5W9fPqRUVFcm5556bdOrUKSkpKUm+9a1vrfX906NHj+S8886rtq229w/FzreuY1K+M15//fWp938+U1O+PjZFvny+PtaWb+7cuTW+F3j00UerxmjKNdhUGfO5DmvLt2rVquSggw5KunTpkrRq1SrZYostkv333z959tlnq41RzDlsaMZCzWGatLKumK+FDclXyNfClStXJiNGjEg222yzpGXLlkmPHj2S733ve2v9ArJYc9jQfIU+niRJklx77bXJ1772taR169bJgAEDkrvuuqvaGMU8njQkX6GOJ5WuuuqqpE2bNtV+9vq8Yr4WNjRjIV8L58+fn4wdOzbp0qVL0rp166RPnz7JJZdcklRUVFSN0dTv+ZoiYyHX4e9///uka9euScuWLZPu3bsn55xzTrX3Umn5kqRw38cNyZfP+avpZ6rPvz9btWpV8sMf/jDZeOONk7Zt2yYHHXRQtf8EVznO5x9Tl/cMxcxY1+NQvvKdd95569ynqV8PK+X+/9AAAAAAAAAA1fjMRAAAAAAAACCVMhEAAAAAAABIpUwEAAAAAAAAUikTAQAAAAAAgFTKRAAAAAAAACCVMhEAAAAAAABIpUwEAAAAAAAAUikTAQAAAAAAgFTKRAAAAIrihhtuiI022qggzzVnzpzo3LlzLFu2bJ37zp49O7p27RorVqwoQDIAAIBsUyYCAAB8hY0dOzZyuVzkcrlo2bJldOrUKfbee++47rrroqKiomA5evbsGZdddlm1bYcffnj85z//Kcjzn3XWWXHKKafEhhtuuM59+/btG1//+tdj0qRJBUgGAACQbcpEAACAr7hRo0bF/Pnz46233ooHHngg9txzzzj11FPj29/+dnz22WcNHjdJkkY9vk2bNrH55ps3+PF19c4778R9990XY8eOrfNjjj/++Ljiiisa9fUBAAB8FSgTAQAAvuJKSkqic+fOseWWW8bOO+8cZ599dtx9993xwAMPxA033BAREW+99VbkcrmYNWtW1eMWL14cuVwupk+fHhER06dPj1wuFw888EAMHDgwSkpK4p///Ge88cYbccABB0SnTp1igw02iMGDB8fDDz9cNc6wYcPi7bffjtNPP73qLMmI9MucXnHFFdG7d+9o1apV9OnTJ2688cZq9+dyufjzn/8cBx10ULRt2za23nrruOeee2r9+v/2t7/FgAEDYsstt6za9vbbb8fo0aNj4403jnbt2kW/fv3iH//4R9X9e++9dyxatCgee+yxuk4zAADAV5IyEQAAYD201157xYABA2Ly5Mn1fuyECRPiwgsvjFdeeSV22GGHWL58eey7774xbdq0eOGFF2LUqFExevToeOeddyIiYvLkydG1a9f4xS9+EfPnz4/58+enjnvnnXfGqaeeGj/+8Y/j5ZdfjpNOOimOP/74ePTRR6vtd/7558dhhx0WL774Yuy7775x9NFHx6JFi2rM+8QTT8SgQYOqbSstLY3Vq1fH448/Hi+99FJcdNFFscEGG1Td36pVq9hxxx3jiSeeqPf8AAAAfJW0KHYAAAAAimPbbbeNF198sd6P+8UvfhF777131e1NNtkkBgwYUHX7l7/8Zdx5551xzz33xLhx42KTTTaJ5s2bx4YbbhidO3eucdyLL744xo4dGz/84Q8jImL8+PHxzDPPxMUXXxx77rln1X5jx46NI488MiIifvOb38Qf/vCHePbZZ2PUqFGp47799ttrlYnvvPNOHHLIIdG/f/+IiNhqq63WelyXLl3i7bffXtd0AAAAfKU5MxEAAGA9lSRJ1SVH6+OLxdzy5cvjJz/5SWy33Xax0UYbxQYbbBCvvPJK1ZmJdfXKK6/EbrvtVm3bbrvtFq+88kq1bTvssEPV39u1axft27ePhQsX1jjuqlWronXr1tW2/ehHP4pf/epXsdtuu8V5552XWqq2adMmVq5cWa+vAQAA4KtGmQgAALCeeuWVV6JXr14REdGs2f+9PUySpOr+NWvWpD6uXbt21W7/5Cc/iTvvvDN+85vfxBNPPBGzZs2K/v37x6efftokuVu2bFntdi6Xi4qKihr379ixY3z88cfVtp144onx5ptvxrHHHhsvvfRSDBo0KC6//PJq+yxatCg222yz/AUHAAD4ElImAgAArIceeeSReOmll+KQQw6JiKgqzT7/eYazZs2q01hPPvlkjB07Ng466KDo379/dO7cOd56661q+7Rq1SrKy8trHWe77baLJ598cq2x+/btW6ccNdlpp51i9uzZa23v1q1b/OAHP4jJkyfHj3/847jmmmuq3f/yyy/HTjvt1KjnBgAA+LLzmYkAAABfcatXr473338/ysvLY8GCBfHggw/GBRdcEN/+9rfjuOOOi4j/u6Tn17/+9bjwwgujV69esXDhwjjnnHPqNP7WW28dkydPjtGjR0cul4tzzz13rTMFe/bsGY8//ngcccQRUVJSEh07dlxrnJ/+9Kdx2GGHxU477RTDhw+Pe++9NyZPnhwPP/xwo77+kSNHxoknnhjl5eXRvHnziIg47bTTYp999oltttkmPv7443j00Udju+22q3rMW2+9FfPmzYvhw4c36rkBAAC+7JyZCAAA8BX34IMPxhZbbBE9e/aMUaNGxaOPPhp/+MMf4u67764q1yIirrvuuvjss89i4MCBcdppp8WvfvWrOo0/adKk2HjjjWPXXXeN0aNHx8iRI2PnnXeuts8vfvGLeOutt6J37941Xjr0wAMPjN///vdx8cUXR79+/eKqq66K66+/PoYNG9bgrz0iYp999okWLVpUKyXLy8ujtLQ0tttuuxg1alRss8028ac//anq/v/5n/+JESNGRI8ePRr13AAAAF92ueTzH4gBAAAAX0FlZWVxzz33xJQpU9a576effhpbb7113HLLLbHbbrsVIB0AAEB2ucwpAAAAX3knnXRSLF68OJYtWxYbbrhhrfu+8847cfbZZysSAQAAwpmJAAAAAAAAQA18ZiIAAAAAAACQSpkIAAAAAAAApFImAgAAAAAAAKmUiQAAAAAAAEAqZSIAAAAAAACQSpkIAAAAAAAApFImAgAAAAAAAKmUiQAAAAAAAEAqZSIAAAAAAACQ6v8DA2e2VPssKSwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_ir_dir = os.path.join(datasets_dir, 'nafp_pp-dataset/aug/ir')\n",
    "merged_ir_paths = sorted(glob(os.path.join(merged_ir_dir, \"*.wav\")))\n",
    "print(len(merged_ir_paths))\n",
    "\n",
    "T_short = 0.5\n",
    "\n",
    "ir_merged_durations = []\n",
    "short_ir_paths = []\n",
    "for ir_path in merged_ir_paths:\n",
    "    ir = audio_processing.load_wav(ir_path, fs=fs)\n",
    "    ir_merged_durations.append(len(ir)/fs)\n",
    "    if len(ir)/fs < T_short:\n",
    "        short_ir_paths.append(ir_path)\n",
    "ir_merged_durations = np.array(ir_merged_durations)\n",
    "\n",
    "print(f\"There are {len(ir_merged_durations)} IRs\")\n",
    "print(f'{len(short_ir_paths)} IRs are shorter than {T_short} s')\n",
    "print(f\"Min duration: {ir_merged_durations.min():.4f} s\")\n",
    "print(f\"Max duration: {ir_merged_durations.max():.4f} s\")\n",
    "print(f\"Mean duration: {ir_merged_durations.mean():.4f} s\")\n",
    "print(f\"Std duration: {ir_merged_durations.std():.4f} s\")\n",
    "print(f\"Median duration: {np.median(ir_merged_durations):.4f} s\")\n",
    "# print(f\"Mode duration: {stats.mode(ir_merged_durations)[0][0]:.4f} s\")\n",
    "print(f\"Total duration: {ir_merged_durations.sum()/60:.4f} m\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(18, 5), constrained_layout=True)\n",
    "fig.suptitle(\"NAFP IR Durations Histogram\")\n",
    "ax.hist(ir_merged_durations, bins=np.arange(0, 21, 0.25))\n",
    "ax.set_yscale('log')\n",
    "ax.set_xticks(np.arange(0, 22, 0.5))\n",
    "ax.set_xlim([0-0.25,21+0.25])\n",
    "ax.set_xlabel(\"Duration (s)\")\n",
    "ax.set_ylabel(\"Count (log)\")\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "T_quantization = 0.25\n",
    "\n",
    "quantized_ir_durations = []\n",
    "for ir_path in merged_ir_paths:\n",
    "    ir_len = audio_processing.check_wav_file(ir_path, fs=fs)\n",
    "    duration = ir_len/fs\n",
    "    quantized_duration = np.ceil(duration/T_quantization).astype(int)*T_quantization\n",
    "    quantized_ir_durations.append(quantized_duration)\n",
    "dur_counter = Counter(quantized_ir_durations)\n",
    "dur_counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "skipped_paths = []\n",
    "kept_paths = []\n",
    "quantized_ir_durations = []\n",
    "for ir_path in merged_ir_paths:\n",
    "    ir_len = audio_processing.check_wav_file(ir_path, fs=fs)\n",
    "    duration = ir_len/fs\n",
    "    quantized_duration = np.ceil(duration/T_quantization).astype(int)*T_quantization\n",
    "    if quantized_duration==8 or quantized_duration==6.25 or quantized_duration==23.25:\n",
    "        skipped_paths.append(ir_path)\n",
    "        continue\n",
    "    quantized_ir_durations.append(quantized_duration)\n",
    "    kept_paths.append(ir_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_paths, test_paths = train_test_split(kept_paths, test_size=0.2, random_state=27, stratify=quantized_ir_durations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ir_tr_durations = []\n",
    "for ir_path in train_paths:\n",
    "    ir = audio_processing.load_wav(ir_path, fs=fs)\n",
    "    ir_tr_durations.append(len(ir)/fs)\n",
    "ir_tr_durations = np.array(ir_tr_durations)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(18, 5), constrained_layout=True)\n",
    "fig.suptitle(\"IR New Train Set Durations Histogram\")\n",
    "ax.hist(ir_tr_durations, bins=np.arange(0, 21, 0.25))\n",
    "ax.set_xticks(np.arange(0, 22, 0.5))\n",
    "ax.set_xlim([0-0.25,21+0.25])\n",
    "ax.set_xlabel(\"Duration (s)\")\n",
    "ax.set_ylabel(\"Count\")\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ir_ts_durations = []\n",
    "for ir_path in test_paths:\n",
    "    ir = audio_processing.load_wav(ir_path, fs=fs)\n",
    "    ir_ts_durations.append(len(ir)/fs)\n",
    "ir_ts_durations = np.array(ir_ts_durations)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(18, 5), constrained_layout=True)\n",
    "fig.suptitle(\"IR New Test Set Durations Histogram\")\n",
    "ax.hist(ir_ts_durations, bins=np.arange(0, 21, 0.25))\n",
    "ax.set_xticks(np.arange(0, 22, 0.5))\n",
    "ax.set_xlim([0-0.25,21+0.25])\n",
    "ax.set_xlabel(\"Duration (s)\")\n",
    "ax.set_ylabel(\"Count\")\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../datasets/nafp_pp-dataset/aug/ir/18a_smoking_room.wav\n",
      "7.94\n",
      "../../datasets/nafp_pp-dataset/aug/ir/spokane_womans_club_ir.wav\n",
      "6.002\n",
      "../../datasets/nafp_pp-dataset/aug/ir/terrys_warehouse_stereo.wav\n",
      "23.01075\n"
     ]
    }
   ],
   "source": [
    "for ir_path in skipped_paths:\n",
    "    print(ir_path)\n",
    "    ir_len = audio_processing.check_wav_file(ir_path, fs=fs)\n",
    "    duration = ir_len/fs\n",
    "    print(duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_paths.append(skipped_paths[2])\n",
    "\n",
    "train_paths.append(skipped_paths[0])\n",
    "train_paths.append(skipped_paths[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ir_tr_durations = []\n",
    "for ir_path in train_paths:\n",
    "    ir = audio_processing.load_wav(ir_path, fs=fs)\n",
    "    ir_tr_durations.append(len(ir)/fs)\n",
    "ir_tr_durations = np.array(ir_tr_durations)\n",
    "\n",
    "ir_ts_durations = []\n",
    "for ir_path in test_paths:\n",
    "    ir = audio_processing.load_wav(ir_path, fs=fs)\n",
    "    ir_ts_durations.append(len(ir)/fs)\n",
    "ir_ts_durations = np.array(ir_ts_durations)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, figsize=(18, 5), constrained_layout=True, sharex=True)\n",
    "fig.suptitle(\"New IR Splits' Duration Histograms\")\n",
    "\n",
    "x_max = max(max(ir_tr_durations), max(ir_ts_durations))\n",
    "\n",
    "ax[0].hist(ir_tr_durations, bins=np.arange(0, x_max+0.5, 0.25))\n",
    "ax[0].set_title(\"Train Set\")\n",
    "\n",
    "ax[1].hist(ir_ts_durations, bins=np.arange(0, x_max+0.5, 0.25))\n",
    "ax[1].set_title(\"Test Set\")\n",
    "ax[1].set_xlabel(\"Duration (s)\")\n",
    "\n",
    "for _x in ax:\n",
    "    _x.set_xticks(np.arange(0, x_max+0.5, 0.5))\n",
    "    _x.set_xlim([0-0.25,x_max+0.5])\n",
    "    _x.set_ylabel(\"Count\")\n",
    "    _x.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(train_paths).intersection(set(test_paths))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Microphone Impulse Response Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Microphone Impulse Resonse Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115\n",
      "38\n",
      "AKGC414Bidirect\n",
      "AKGC414Cardioid\n",
      "AKGC414Hyper\n",
      "AKGC414Omni\n",
      "AKGC451Cardioid\n",
      "AKGC451Omni\n",
      "AKGD112\n",
      "AKGD12\n",
      "Coles4038\n",
      "DPA4006\n",
      "ElectroVoiceRE20\n",
      "NTiM2211\n",
      "NeumannU47FET\n",
      "NeumannU87AiBidirect\n",
      "NeumannU87AiCardioid\n",
      "NeumannU87AiOmni\n",
      "RodeK2Bidirect\n",
      "RodeK2Cardioid\n",
      "RodeK2Omni\n",
      "RodeNT2ABidirect\n",
      "RodeNT2ACardioid\n",
      "RodeNT2AOmni\n",
      "RodeNTG8\n",
      "RodeNTR\n",
      "RodeReporter\n",
      "RoyerR121\n",
      "SchoepsCMC5UBidirect\n",
      "SchoepsCMC5UCardioid\n",
      "SchoepsCMC5UOmni\n",
      "SchoepsCMC6\n",
      "SchoepsCMIT5U\n",
      "SennheiserMD441\n",
      "ShureBeta52\n",
      "ShureSM57\n",
      "ShureSM58\n",
      "SonyC800Cardioid\n",
      "SonyC800Omni\n",
      "SonyECM670\n"
     ]
    }
   ],
   "source": [
    "microphone_ir_dataset_dir = os.path.join(datasets_dir, \"IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs\")\n",
    "microphone_direct_distance_names = sorted(os.listdir(microphone_ir_dataset_dir))\n",
    "print(len(microphone_direct_distance_names))\n",
    "\n",
    "microphone_direct_names = sorted(list(set([name.split(\"_\")[0] for name in microphone_direct_distance_names])))\n",
    "print(len(microphone_direct_names))\n",
    "print('\\n'.join(microphone_direct_names))\n",
    "\n",
    "# microphone_ir_paths = sorted(glob(os.path.join(microphone_ir_dataset_dir, \"*.wav\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "mic_direct_dict = dict()\n",
    "for microphone_direct_distance in microphone_direct_distance_names:\n",
    "    if '_' not in microphone_direct_distance:\n",
    "        print(microphone_direct_distance)\n",
    "    else:\n",
    "        lst = microphone_direct_distance.split(\"_\")\n",
    "        dst = lst[-1]\n",
    "        rest = '_'.join(lst[:-1])\n",
    "        if rest not in mic_direct_dict:\n",
    "            mic_direct_dict[rest] = []\n",
    "        mic_direct_dict[rest].append(dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AKGC414Bidirect': ['125cm', '50cm', '5m'],\n",
       " 'AKGC414Cardioid': ['125cm', '50cm', '5m'],\n",
       " 'AKGC414Hyper': ['125cm', '50cm', '5m'],\n",
       " 'AKGC414Omni': ['125cm', '50cm', '5m'],\n",
       " 'AKGC451Cardioid': ['125cm', '50cm', '5m'],\n",
       " 'AKGC451Omni': ['125cm', '50cm', '5m'],\n",
       " 'AKGD112': ['125cm', '50cm', '5m'],\n",
       " 'AKGD12': ['125cm', '50cm', '5m'],\n",
       " 'Coles4038': ['125cm', '50cm', '5m'],\n",
       " 'DPA4006': ['125cm', '50cm', '5m'],\n",
       " 'ElectroVoiceRE20': ['125cm', '50cm', '5m'],\n",
       " 'NTiM2211': ['125cm', '50cm', '5m'],\n",
       " 'NeumannU47FET': ['125cm', '50cm'],\n",
       " 'NeumannU87AiBidirect': ['125cm', '50cm', '5m'],\n",
       " 'NeumannU87AiCardioid': ['125cm', '50cm', '5m'],\n",
       " 'NeumannU87AiOmni': ['125cm', '50cm', '5m'],\n",
       " 'RodeK2Bidirect': ['125cm', '50cm', '5m'],\n",
       " 'RodeK2Cardioid': ['125cm', '50cm', '5m'],\n",
       " 'RodeK2Omni': ['125cm', '50cm', '5m'],\n",
       " 'RodeNT2ABidirect': ['125cm', '50cm', '5m'],\n",
       " 'RodeNT2ACardioid': ['125cm', '50cm', '5m'],\n",
       " 'RodeNT2AOmni': ['125cm', '50cm', '5m'],\n",
       " 'RodeNTG8': ['125cm', '5m'],\n",
       " 'RodeNTR': ['125cm', '50cm', '5m'],\n",
       " 'RodeReporter': ['125cm', '50cm', '5m'],\n",
       " 'RoyerR121': ['125cm', '50cm', '5m'],\n",
       " 'SchoepsCMC5UBidirect': ['125cm', '50cm', '5m'],\n",
       " 'SchoepsCMC5UCardioid': ['125cm', '50cm', '5m'],\n",
       " 'SchoepsCMC5UOmni': ['125cm', '50cm', '5m'],\n",
       " 'SchoepsCMC6_MK2H': ['125cm', '50cm', '5m'],\n",
       " 'SchoepsCMC6_MK2H_KA40': ['125cm', '50cm', '5m'],\n",
       " 'SchoepsCMIT5U': ['125cm', '50cm', '5m'],\n",
       " 'SennheiserMD441': ['125cm', '50cm', '5m'],\n",
       " 'ShureBeta52': ['125cm', '50cm', '5m'],\n",
       " 'ShureSM57': ['125cm', '50cm', '5m'],\n",
       " 'ShureSM58': ['125cm', '50cm', '5m'],\n",
       " 'SonyC800Cardioid': ['125cm', '50cm', '5m'],\n",
       " 'SonyC800Omni': ['125cm', '50cm', '5m'],\n",
       " 'SonyECM670': ['125cm', '50cm', '5m']}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mic_direct_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "mic_names = [\n",
    "    'AKGC414',\n",
    "    'AKGC451',\n",
    "    'AKGD112',\n",
    "    'AKGD12',\n",
    "    'Coles4038',\n",
    "    'DPA4006',\n",
    "    'ElectroVoiceRE20',\n",
    "    'NTiM2211',\n",
    "    'NeumannU47FET',\n",
    "    'NeumannU87Ai',\n",
    "    'RodeK2',\n",
    "    'RodeNT2A',\n",
    "    'RodeNTG8',\n",
    "    'RodeNTR',\n",
    "    'RodeReporter',\n",
    "    'RoyerR121',\n",
    "    'SchoepsCMC5U',\n",
    "    'SchoepsCMC6',\n",
    "    'SchoepsCMIT5U',\n",
    "    'SennheiserMD441',\n",
    "    'ShureBeta52',\n",
    "    'ShureSM57',\n",
    "    'ShureSM58',\n",
    "    'SonyC800',\n",
    "    'SonyECM670',\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "mic_dict = dict()\n",
    "for mic_name in mic_names:\n",
    "    mic_dict[mic_name] = []\n",
    "\n",
    "    for k,v in mic_direct_dict.items():\n",
    "        if mic_name in k:\n",
    "            mic_dict[mic_name].append({k:v})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"AKGC414\": [\n",
      "        {\n",
      "            \"AKGC414Bidirect\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        },\n",
      "        {\n",
      "            \"AKGC414Cardioid\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        },\n",
      "        {\n",
      "            \"AKGC414Hyper\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        },\n",
      "        {\n",
      "            \"AKGC414Omni\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"AKGC451\": [\n",
      "        {\n",
      "            \"AKGC451Cardioid\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        },\n",
      "        {\n",
      "            \"AKGC451Omni\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"AKGD112\": [\n",
      "        {\n",
      "            \"AKGD112\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"AKGD12\": [\n",
      "        {\n",
      "            \"AKGD12\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"Coles4038\": [\n",
      "        {\n",
      "            \"Coles4038\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"DPA4006\": [\n",
      "        {\n",
      "            \"DPA4006\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"ElectroVoiceRE20\": [\n",
      "        {\n",
      "            \"ElectroVoiceRE20\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"NTiM2211\": [\n",
      "        {\n",
      "            \"NTiM2211\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"NeumannU47FET\": [\n",
      "        {\n",
      "            \"NeumannU47FET\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"NeumannU87Ai\": [\n",
      "        {\n",
      "            \"NeumannU87AiBidirect\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        },\n",
      "        {\n",
      "            \"NeumannU87AiCardioid\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        },\n",
      "        {\n",
      "            \"NeumannU87AiOmni\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"RodeK2\": [\n",
      "        {\n",
      "            \"RodeK2Bidirect\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        },\n",
      "        {\n",
      "            \"RodeK2Cardioid\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        },\n",
      "        {\n",
      "            \"RodeK2Omni\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"RodeNT2A\": [\n",
      "        {\n",
      "            \"RodeNT2ABidirect\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        },\n",
      "        {\n",
      "            \"RodeNT2ACardioid\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        },\n",
      "        {\n",
      "            \"RodeNT2AOmni\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"RodeNTG8\": [\n",
      "        {\n",
      "            \"RodeNTG8\": [\n",
      "                \"125cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"RodeNTR\": [\n",
      "        {\n",
      "            \"RodeNTR\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"RodeReporter\": [\n",
      "        {\n",
      "            \"RodeReporter\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"RoyerR121\": [\n",
      "        {\n",
      "            \"RoyerR121\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"SchoepsCMC5U\": [\n",
      "        {\n",
      "            \"SchoepsCMC5UBidirect\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        },\n",
      "        {\n",
      "            \"SchoepsCMC5UCardioid\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        },\n",
      "        {\n",
      "            \"SchoepsCMC5UOmni\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"SchoepsCMC6\": [\n",
      "        {\n",
      "            \"SchoepsCMC6_MK2H\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        },\n",
      "        {\n",
      "            \"SchoepsCMC6_MK2H_KA40\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"SchoepsCMIT5U\": [\n",
      "        {\n",
      "            \"SchoepsCMIT5U\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"SennheiserMD441\": [\n",
      "        {\n",
      "            \"SennheiserMD441\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"ShureBeta52\": [\n",
      "        {\n",
      "            \"ShureBeta52\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"ShureSM57\": [\n",
      "        {\n",
      "            \"ShureSM57\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"ShureSM58\": [\n",
      "        {\n",
      "            \"ShureSM58\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"SonyC800\": [\n",
      "        {\n",
      "            \"SonyC800Cardioid\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        },\n",
      "        {\n",
      "            \"SonyC800Omni\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"SonyECM670\": [\n",
      "        {\n",
      "            \"SonyECM670\": [\n",
      "                \"125cm\",\n",
      "                \"50cm\",\n",
      "                \"5m\"\n",
      "            ]\n",
      "        }\n",
      "    ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(json.dumps(mic_dict, indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8496\n"
     ]
    }
   ],
   "source": [
    "mic_ir_dataset_paths = sorted(glob(os.path.join(microphone_ir_dataset_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "print(len(mic_ir_dataset_paths))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analyze Durations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 8496 IRs\n",
      "Min duration: 10.0000 s\n",
      "Max duration: 10.0000 s\n",
      "Mean duration: 10.0000 s\n",
      "Std duration: 0.0000 s\n",
      "Median duration: 10.0000 s\n",
      "Total duration: 1416.0000 m\n",
      "8496 files are longer than 1.0 s\n",
      "Total duration of these files: 1416.0000 m\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mic_ir_dataset_durations = []\n",
    "for ir_path in mic_ir_dataset_paths:\n",
    "    ir_len = audio_processing.check_wav_file(ir_path, fs=fs)\n",
    "    mic_ir_dataset_durations.append(ir_len/fs)\n",
    "# print(\"Train\")\n",
    "print(f\"There are {len(mic_ir_dataset_durations)} IRs\")\n",
    "mic_ir_dataset_durations = np.array(mic_ir_dataset_durations)\n",
    "print(f\"Min duration: {mic_ir_dataset_durations.min():.4f} s\")\n",
    "print(f\"Max duration: {mic_ir_dataset_durations.max():.4f} s\")\n",
    "print(f\"Mean duration: {mic_ir_dataset_durations.mean():.4f} s\")\n",
    "print(f\"Std duration: {mic_ir_dataset_durations.std():.4f} s\")\n",
    "print(f\"Median duration: {np.median(mic_ir_dataset_durations):.4f} s\")\n",
    "print(f\"Total duration: {mic_ir_dataset_durations.sum()/60:.4f} m\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(18, 5), constrained_layout=True)\n",
    "fig.suptitle(\"Microphone IR Dataset Duration Histogram\")\n",
    "\n",
    "x_max = max(mic_ir_dataset_durations)\n",
    "delta_t = 0.25\n",
    "\n",
    "ax.hist(mic_ir_dataset_durations, bins=np.arange(0, x_max+delta_t, delta_t))\n",
    "# ax.set_title(\"Train Set\")\n",
    "ax.set_xticks(np.arange(0, x_max+delta_t, delta_t))\n",
    "ax.set_xlim([0-(delta_t/2),x_max+(delta_t/2)])\n",
    "ax.set_ylabel(\"Count\")\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listen to the microphones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "audio_path = nafp_music_db_paths[100]\n",
    "audio = audio_processing.load_wav(audio_path, fs=fs)\n",
    "print(audio_path)\n",
    "ipd.display(ipd.Audio(audio, rate=fs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_ir_path = mic_ir_dataset_paths[5005]\n",
    "\n",
    "print('IR')\n",
    "print(os.path.basename(m_ir_path))\n",
    "m_ir = audio_processing.load_wav(m_ir_path, fs=fs)\n",
    "print(len(m_ir)/fs)\n",
    "ipd.display(ipd.Audio(m_ir, rate=fs))\n",
    "\n",
    "print('Music')\n",
    "ipd.display(ipd.Audio(audio, rate=fs))\n",
    "\n",
    "print('Music with Microphone')\n",
    "audio_mic = audio_processing.convolve_with_IR(audio, m_ir)\n",
    "ipd.display(ipd.Audio(audio_mic, rate=fs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Your Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Move files around"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC414Bidirect_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC414/AKGC414Bidirect/AKGC414Bidirect_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC414Bidirect_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC414/AKGC414Bidirect/AKGC414Bidirect_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC414Bidirect_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC414/AKGC414Bidirect/AKGC414Bidirect_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC414Cardioid_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC414/AKGC414Cardioid/AKGC414Cardioid_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC414Cardioid_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC414/AKGC414Cardioid/AKGC414Cardioid_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC414Cardioid_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC414/AKGC414Cardioid/AKGC414Cardioid_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC414Hyper_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC414/AKGC414Hyper/AKGC414Hyper_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC414Hyper_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC414/AKGC414Hyper/AKGC414Hyper_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC414Hyper_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC414/AKGC414Hyper/AKGC414Hyper_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC414Omni_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC414/AKGC414Omni/AKGC414Omni_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC414Omni_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC414/AKGC414Omni/AKGC414Omni_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC414Omni_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC414/AKGC414Omni/AKGC414Omni_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC451Cardioid_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC451/AKGC451Cardioid/AKGC451Cardioid_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC451Cardioid_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC451/AKGC451Cardioid/AKGC451Cardioid_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC451Cardioid_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC451/AKGC451Cardioid/AKGC451Cardioid_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC451Omni_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC451/AKGC451Omni/AKGC451Omni_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC451Omni_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC451/AKGC451Omni/AKGC451Omni_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGC451Omni_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC451/AKGC451Omni/AKGC451Omni_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGD112_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGD112/AKGD112/AKGD112_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGD112_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGD112/AKGD112/AKGD112_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGD112_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGD112/AKGD112/AKGD112_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGD12_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGD12/AKGD12/AKGD12_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGD12_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGD12/AKGD12/AKGD12_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/AKGD12_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGD12/AKGD12/AKGD12_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/Coles4038_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/Coles4038/Coles4038/Coles4038_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/Coles4038_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/Coles4038/Coles4038/Coles4038_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/Coles4038_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/Coles4038/Coles4038/Coles4038_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/DPA4006_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/DPA4006/DPA4006/DPA4006_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/DPA4006_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/DPA4006/DPA4006/DPA4006_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/DPA4006_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/DPA4006/DPA4006/DPA4006_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/ElectroVoiceRE20_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/ElectroVoiceRE20/ElectroVoiceRE20/ElectroVoiceRE20_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/ElectroVoiceRE20_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/ElectroVoiceRE20/ElectroVoiceRE20/ElectroVoiceRE20_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/ElectroVoiceRE20_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/ElectroVoiceRE20/ElectroVoiceRE20/ElectroVoiceRE20_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/NTiM2211_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NTiM2211/NTiM2211/NTiM2211_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/NTiM2211_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NTiM2211/NTiM2211/NTiM2211_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/NTiM2211_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NTiM2211/NTiM2211/NTiM2211_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/NeumannU47FET_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NeumannU47FET/NeumannU47FET/NeumannU47FET_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/NeumannU47FET_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NeumannU47FET/NeumannU47FET/NeumannU47FET_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/NeumannU87AiBidirect_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NeumannU87Ai/NeumannU87AiBidirect/NeumannU87AiBidirect_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/NeumannU87AiBidirect_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NeumannU87Ai/NeumannU87AiBidirect/NeumannU87AiBidirect_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/NeumannU87AiBidirect_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NeumannU87Ai/NeumannU87AiBidirect/NeumannU87AiBidirect_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/NeumannU87AiCardioid_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NeumannU87Ai/NeumannU87AiCardioid/NeumannU87AiCardioid_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/NeumannU87AiCardioid_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NeumannU87Ai/NeumannU87AiCardioid/NeumannU87AiCardioid_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/NeumannU87AiCardioid_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NeumannU87Ai/NeumannU87AiCardioid/NeumannU87AiCardioid_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/NeumannU87AiOmni_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NeumannU87Ai/NeumannU87AiOmni/NeumannU87AiOmni_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/NeumannU87AiOmni_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NeumannU87Ai/NeumannU87AiOmni/NeumannU87AiOmni_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/NeumannU87AiOmni_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NeumannU87Ai/NeumannU87AiOmni/NeumannU87AiOmni_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeK2Bidirect_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeK2/RodeK2Bidirect/RodeK2Bidirect_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeK2Bidirect_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeK2/RodeK2Bidirect/RodeK2Bidirect_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeK2Bidirect_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeK2/RodeK2Bidirect/RodeK2Bidirect_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeK2Cardioid_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeK2/RodeK2Cardioid/RodeK2Cardioid_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeK2Cardioid_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeK2/RodeK2Cardioid/RodeK2Cardioid_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeK2Cardioid_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeK2/RodeK2Cardioid/RodeK2Cardioid_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeK2Omni_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeK2/RodeK2Omni/RodeK2Omni_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeK2Omni_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeK2/RodeK2Omni/RodeK2Omni_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeK2Omni_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeK2/RodeK2Omni/RodeK2Omni_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeNT2ABidirect_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNT2A/RodeNT2ABidirect/RodeNT2ABidirect_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeNT2ABidirect_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNT2A/RodeNT2ABidirect/RodeNT2ABidirect_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeNT2ABidirect_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNT2A/RodeNT2ABidirect/RodeNT2ABidirect_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeNT2ACardioid_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNT2A/RodeNT2ACardioid/RodeNT2ACardioid_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeNT2ACardioid_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNT2A/RodeNT2ACardioid/RodeNT2ACardioid_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeNT2ACardioid_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNT2A/RodeNT2ACardioid/RodeNT2ACardioid_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeNT2AOmni_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNT2A/RodeNT2AOmni/RodeNT2AOmni_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeNT2AOmni_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNT2A/RodeNT2AOmni/RodeNT2AOmni_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeNT2AOmni_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNT2A/RodeNT2AOmni/RodeNT2AOmni_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeNTG8_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNTG8/RodeNTG8/RodeNTG8_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeNTG8_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNTG8/RodeNTG8/RodeNTG8_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeNTR_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNTR/RodeNTR/RodeNTR_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeNTR_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNTR/RodeNTR/RodeNTR_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeNTR_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNTR/RodeNTR/RodeNTR_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeReporter_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeReporter/RodeReporter/RodeReporter_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeReporter_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeReporter/RodeReporter/RodeReporter_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RodeReporter_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeReporter/RodeReporter/RodeReporter_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RoyerR121_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RoyerR121/RoyerR121/RoyerR121_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RoyerR121_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RoyerR121/RoyerR121/RoyerR121_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/RoyerR121_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RoyerR121/RoyerR121/RoyerR121_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMC5UBidirect_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC5U/SchoepsCMC5UBidirect/SchoepsCMC5UBidirect_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMC5UBidirect_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC5U/SchoepsCMC5UBidirect/SchoepsCMC5UBidirect_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMC5UBidirect_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC5U/SchoepsCMC5UBidirect/SchoepsCMC5UBidirect_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMC5UCardioid_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC5U/SchoepsCMC5UCardioid/SchoepsCMC5UCardioid_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMC5UCardioid_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC5U/SchoepsCMC5UCardioid/SchoepsCMC5UCardioid_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMC5UCardioid_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC5U/SchoepsCMC5UCardioid/SchoepsCMC5UCardioid_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMC5UOmni_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC5U/SchoepsCMC5UOmni/SchoepsCMC5UOmni_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMC5UOmni_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC5U/SchoepsCMC5UOmni/SchoepsCMC5UOmni_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMC5UOmni_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC5U/SchoepsCMC5UOmni/SchoepsCMC5UOmni_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMC6_MK2H_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC6/SchoepsCMC6_MK2H/SchoepsCMC6_MK2H_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMC6_MK2H_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC6/SchoepsCMC6_MK2H/SchoepsCMC6_MK2H_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMC6_MK2H_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC6/SchoepsCMC6_MK2H/SchoepsCMC6_MK2H_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMC6_MK2H_KA40_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC6/SchoepsCMC6_MK2H_KA40/SchoepsCMC6_MK2H_KA40_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMC6_MK2H_KA40_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC6/SchoepsCMC6_MK2H_KA40/SchoepsCMC6_MK2H_KA40_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMC6_MK2H_KA40_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC6/SchoepsCMC6_MK2H_KA40/SchoepsCMC6_MK2H_KA40_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMIT5U_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMIT5U/SchoepsCMIT5U/SchoepsCMIT5U_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMIT5U_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMIT5U/SchoepsCMIT5U/SchoepsCMIT5U_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SchoepsCMIT5U_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMIT5U/SchoepsCMIT5U/SchoepsCMIT5U_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SennheiserMD441_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SennheiserMD441/SennheiserMD441/SennheiserMD441_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SennheiserMD441_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SennheiserMD441/SennheiserMD441/SennheiserMD441_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SennheiserMD441_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SennheiserMD441/SennheiserMD441/SennheiserMD441_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/ShureBeta52_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/ShureBeta52/ShureBeta52/ShureBeta52_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/ShureBeta52_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/ShureBeta52/ShureBeta52/ShureBeta52_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/ShureBeta52_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/ShureBeta52/ShureBeta52/ShureBeta52_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/ShureSM57_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/ShureSM57/ShureSM57/ShureSM57_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/ShureSM57_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/ShureSM57/ShureSM57/ShureSM57_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/ShureSM57_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/ShureSM57/ShureSM57/ShureSM57_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/ShureSM58_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/ShureSM58/ShureSM58/ShureSM58_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/ShureSM58_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/ShureSM58/ShureSM58/ShureSM58_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/ShureSM58_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/ShureSM58/ShureSM58/ShureSM58_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SonyC800Cardioid_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SonyC800/SonyC800Cardioid/SonyC800Cardioid_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SonyC800Cardioid_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SonyC800/SonyC800Cardioid/SonyC800Cardioid_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SonyC800Cardioid_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SonyC800/SonyC800Cardioid/SonyC800Cardioid_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SonyC800Omni_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SonyC800/SonyC800Omni/SonyC800Omni_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SonyC800Omni_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SonyC800/SonyC800Omni/SonyC800Omni_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SonyC800Omni_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SonyC800/SonyC800Omni/SonyC800Omni_5m\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SonyECM670_125cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SonyECM670/SonyECM670/SonyECM670_125cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SonyECM670_50cm\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SonyECM670/SonyECM670/SonyECM670_50cm\n",
      "\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs/SonyECM670_5m\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SonyECM670/SonyECM670/SonyECM670_5m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "new_microphone_ir_dataset_dir = os.path.join(os.path.dirname(microphone_ir_dataset_dir), \"Normalised_IRs-grouped\")\n",
    "for mic, mic_direction_dcts in mic_dict.items():\n",
    "    new_mic_dir = os.path.join(new_microphone_ir_dataset_dir, mic)\n",
    "    for mic_direction_dct in mic_direction_dcts:\n",
    "        for mic_direction, distances in mic_direction_dct.items():\n",
    "            new_mic_direct_dir = os.path.join(new_mic_dir, mic_direction)\n",
    "            for dst in distances:\n",
    "                new_path = os.path.join(new_mic_direct_dir, f\"{mic_direction}_{dst}\")\n",
    "                old_path = os.path.join(microphone_ir_dataset_dir, f\"{mic_direction}_{dst}\")\n",
    "                print(old_path)\n",
    "                print(new_path)\n",
    "                print()\n",
    "                # shutil.copytree(old_path, new_path, dirs_exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Delete some degrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_names = os.listdir(\"../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC414/AKGC414Bidirect/AKGC414Bidirect_125cm/24bit/\")\n",
    "degrees = sorted([int(name.split(\"_\")[-2].replace('Deg', '')) for name in file_names])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 5,\n",
       " 10,\n",
       " 15,\n",
       " 20,\n",
       " 25,\n",
       " 30,\n",
       " 35,\n",
       " 40,\n",
       " 45,\n",
       " 50,\n",
       " 55,\n",
       " 60,\n",
       " 65,\n",
       " 70,\n",
       " 75,\n",
       " 80,\n",
       " 85,\n",
       " 90,\n",
       " 95,\n",
       " 100,\n",
       " 105,\n",
       " 110,\n",
       " 115,\n",
       " 120,\n",
       " 125,\n",
       " 130,\n",
       " 135,\n",
       " 140,\n",
       " 145,\n",
       " 150,\n",
       " 155,\n",
       " 160,\n",
       " 165,\n",
       " 170,\n",
       " 175,\n",
       " 180,\n",
       " 185,\n",
       " 190,\n",
       " 195,\n",
       " 200,\n",
       " 205,\n",
       " 210,\n",
       " 215,\n",
       " 220,\n",
       " 225,\n",
       " 230,\n",
       " 235,\n",
       " 240,\n",
       " 245,\n",
       " 250,\n",
       " 255,\n",
       " 260,\n",
       " 265,\n",
       " 270,\n",
       " 275,\n",
       " 280,\n",
       " 285,\n",
       " 290,\n",
       " 295,\n",
       " 300,\n",
       " 305,\n",
       " 310,\n",
       " 315,\n",
       " 320,\n",
       " 325,\n",
       " 330,\n",
       " 335,\n",
       " 340,\n",
       " 345,\n",
       " 350,\n",
       " 355]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "degrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8496\n"
     ]
    }
   ],
   "source": [
    "mir_paths = sorted(glob(os.path.join(new_microphone_ir_dataset_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "print(len(mir_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "for mir_path in mir_paths:\n",
    "    if not os.path.isfile(mir_path):\n",
    "        continue\n",
    "    try:\n",
    "        deg = int(mir_path.split(\"_\")[-2].replace('Deg', ''))\n",
    "    except ValueError:\n",
    "        deg = int(os.path.splitext(mir_path)[0].split(\"_\")[-1].replace('Deg', ''))\n",
    "    except:\n",
    "        print(mir_path)\n",
    "        continue\n",
    "    if deg % 60 != 0:\n",
    "        os.remove(mir_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "mir_paths = sorted(glob(os.path.join(new_microphone_ir_dataset_dir, \"**\", \"*.wav\"), recursive=True))\n",
    "# print(len(mir_paths))\n",
    "\n",
    "degrees_ = []\n",
    "for mir_path in mir_paths:\n",
    "    try:\n",
    "        deg = int(mir_path.split(\"_\")[-2].replace('Deg', ''))\n",
    "    except ValueError:\n",
    "        deg = int(os.path.splitext(mir_path)[0].split(\"_\")[-1].replace('Deg', ''))\n",
    "    except:\n",
    "        print(mir_path)\n",
    "        continue\n",
    "    degrees_.append(deg)\n",
    "degrees_ = set(degrees_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "708"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mir_paths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Split to Dev and Eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_mir_names, test_mir_names = train_test_split(mic_names, test_size=0.2, random_state=27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "564\n",
      "144\n"
     ]
    }
   ],
   "source": [
    "train_mir_paths = []\n",
    "for mic_name in train_mir_names:\n",
    "    train_mir_paths.extend(sorted(glob(os.path.join(new_microphone_ir_dataset_dir, mic_name, \"**\", \"*.wav\"), recursive=True)))\n",
    "print(len(train_mir_paths))\n",
    "\n",
    "test_mir_paths = []\n",
    "for mic_name in test_mir_names:\n",
    "    test_mir_paths.extend(sorted(glob(os.path.join(new_microphone_ir_dataset_dir, mic_name, \"**\", \"*.wav\"), recursive=True)))\n",
    "print(len(test_mir_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNTG8\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NeumannU87Ai\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/ShureSM57\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC414\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RoyerR121\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGD12\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/ElectroVoiceRE20\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/Coles4038\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/ShureSM58\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SonyECM670\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeReporter\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGC451\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeK2\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/AKGD112\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC6\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/RodeNTR\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/DPA4006\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SchoepsCMC5U\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/NeumannU47FET\n",
      "../../datasets/IR/microphone/Microphone_Impulse_Responses-8khz_16bit/Normalised_IRs-grouped/SennheiserMD441\n"
     ]
    }
   ],
   "source": [
    "new_microphone_ir_dataset_dir_tr = os.path.join(nafp_pp_dataset_dir, 'aug', 'microphone_ir', 'dev')\n",
    "new_microphone_ir_dataset_dir_ts = os.path.join(nafp_pp_dataset_dir, 'aug', 'microphone_ir', 'test')\n",
    "os.makedirs(new_microphone_ir_dataset_dir_tr, exist_ok=True)\n",
    "os.makedirs(new_microphone_ir_dataset_dir_ts, exist_ok=True)\n",
    "\n",
    "for mic_name in train_mir_names:\n",
    "    mic_dir = os.path.join(new_microphone_ir_dataset_dir, mic_name)\n",
    "    shutil.copytree(mic_dir, os.path.join(new_microphone_ir_dataset_dir_tr, mic_name), dirs_exist_ok=True)\n",
    "\n",
    "for mic_name in test_mir_names:\n",
    "    mic_dir = os.path.join(new_microphone_ir_dataset_dir, mic_name)\n",
    "    # shutil.copytree(mic_dir, os.path.join(new_microphone_ir_dataset_dir_ts, mic_name), dirs_exist_ok=True)"
   ]
  }
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