speaker-design-notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" Basic imports and configurations for efficient numerical calculations and beautiful plots.\n",
    "\"\"\"\n",
    "\n",
    "# Imports from __future__ in case we're running Python 2\n",
    "from __future__ import division, print_function\n",
    "from __future__ import absolute_import, unicode_literals\n",
    "\n",
    "# Our numerical workhorses\n",
    "import numpy as np\n",
    "# import scipy.integrate\n",
    "\n",
    "# Import pyplot for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import widgets\n",
    "\n",
    "# Seaborn, useful for graphics\n",
    "import seaborn as sns\n",
    "\n",
    "# Magic function to make matplotlib inline; other style specs must come AFTER\n",
    "%matplotlib inline\n",
    "\n",
    "# This enables SVG graphics inline.  There is a bug, so uncomment if it works.\n",
    "%config InlineBackend.figure_formats = {'svg',}\n",
    "\n",
    "# This enables high resolution PNGs. SVG is preferred, but has problems\n",
    "# rendering vertical and horizontal lines\n",
    "%config InlineBackend.figure_formats = {'png', 'retina'}\n",
    "\n",
    "rc = {'lines.linewidth': 2, \n",
    "      'axes.labelsize': 18, \n",
    "      'axes.titlesize': 18, \n",
    "      'axes.facecolor': 'DFDFE5',\n",
    "      'axes.autolimit_mode': 'round_numbers'}\n",
    "sns.set_context('notebook', rc=rc)\n",
    "sns.set_style('darkgrid', rc=rc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Speaker(object):\n",
    "    def __init__(self, params):\n",
    "        self.name = params.get('name')  # Name for speaker parameter set\n",
    "        self.sensitivity = params.get('sensitivity')  # sensitivity of the speaker, SPL at 1 meter for an input of 1 watt\n",
    "        \n",
    "        # fundamental parameters\n",
    "        self.sd = params.get('sd')  # driver diaphragm area in square meters\n",
    "        self.mms = params.get('mms')  # driver mass\n",
    "        self.cms = params.get('cms')  # driver compliance\n",
    "        self.rms = params.get('rms')  # mechanical resistance\n",
    "        self.re = params.get('re')  # driver impedance in ohm\n",
    "        self.le = params.get('le')  # voice coil inductance\n",
    "        self.bl = params.get('bl')  # driver magnetic field strength\n",
    "        self.vas = params.get('vas')  # volume of air necessary for equal compliance as CMS\n",
    "        \n",
    "        # small parameters\n",
    "        self.fs = params.get('fs')  # resonance frequency\n",
    "        self.qts = params.get('qts')  # overall quality factor\n",
    "        self.qms = params.get('qms')  # mechanical quality factor\n",
    "        self.qes = params.get('qes')  # electrical quality factor\n",
    "        \n",
    "        # large parameters\n",
    "        self.vd = params.get('vd')  # peak displacement volume\n",
    "        self.xmax = params.get('xmax')  # max driver excursion in millimeters\n",
    "        \n",
    "    \n",
    "    def __repr__(self):\n",
    "        return '\\n'.join(item+': '+str(self.__dict__[item]) for item in self.__dict__)\n",
    "            \n",
    "def fs(mms, cms):\n",
    "    \"\"\" Resonance frequency is inversely proportional to diaphragm's mass and suspension's compliance.\n",
    "    mms = driver mass in gramm\n",
    "    cms = driver compliance in mm/N\n",
    "    \"\"\"\n",
    "    return 1. / (2. * np.pi * np.sqrt(mms * cms))\n",
    "\n",
    "\n",
    "def qts(qms, qes):\n",
    "    \"\"\" Quality factors are calculated from their mechanical and electrical inverse.\n",
    "    \"\"\"\n",
    "    return 1. / (1. / qms + 1. / qes)\n",
    "\n",
    "\n",
    "def qms(qts, qes):\n",
    "    return 1. / (1. / qts - 1. / qes)\n",
    "\n",
    "\n",
    "def qes(qts, qms):\n",
    "    return 1. / (1. / qts - 1. / qms)\n",
    "\n",
    "\n",
    "def vas(cms, sd):\n",
    "    \"\"\" Calculate VAS from driver compliance and diaphragm diameter.\n",
    "    Return volume in cubic meters, multiply by 1000 to get litres\n",
    "    \"\"\"\n",
    "    air_density = 1.184 # k/m^3 at 25°C\n",
    "    speed_sound = 346.1 # m/s at 25°C\n",
    "    return air_density * speed_sound**2 * cms * sd**2\n",
    "\n",
    "\n",
    "def calc_params(speaker_params):\n",
    "    \"\"\"Calculates system parameters from physical parameters.\n",
    "    \"\"\"\n",
    "    p = speaker_params\n",
    "\n",
    "    p.qes = qes(p.qts, p.qms)\n",
    "    p.qms = qms(p.qts, p.qes)\n",
    "    p.qts = qts(p.qms, p.qes)\n",
    "    \n",
    "    p.rms = p.bl**2 / (p.qms / p.qes * p.re)\n",
    "    \n",
    "    p.cms = 1. / 2. / np.pi / p.fs / p.qms / p.rms * 1000 # mm / N\n",
    "    p.mms = 1. / (p.cms * (2. * np.pi * p.fs)**2) * 1000  # grams\n",
    "    \n",
    "    p.fs = fs(p.mms/1000, p.cms/1000)\n",
    "    p.vas = vas(p.cms/1000, p.sd/10000)\n",
    "    \n",
    "\n",
    "    \n",
    "def calc_sensitivity(speaker_params):\n",
    "    p = speaker_params\n",
    "    \n",
    "    p.sensitivity = (p.sd / 10000.)**2 * 1.184 / 346.1 / 2. / np.pi / p.re / (p.mms / 1000 / p.bl ** 2) ** 2 / p.bl ** 2\n",
    "\n",
    "\n",
    "\n",
    "def calculate_impedance(speaker_params, v_box=np.inf, l_over_a=np.inf):\n",
    "    \"\"\"Calculate impedance magnitude, impedance phase, SPL magnitude,\n",
    "    acoustic output phase, and acoustic output group delay.\n",
    "    \n",
    "    Code for calculations of transferfunction and pre-calculations from\n",
    "    Scott Howard's https://github.com/maqifrnswa/scimpy\n",
    "    \n",
    "    l_over_a = Port/Vent Length to Area Ratio\n",
    "    v_box = \"Box Volume\"\n",
    "    \"\"\"\n",
    "    p = speaker_params\n",
    "    \n",
    "    rms = p.bl**2 / (p.qms / p.qes * p.re)\n",
    "    res = p.bl**2 / rms\n",
    "    les = p.bl**2 * p.cms / 1000.\n",
    "    ces = p.mms / 1000. / p.bl**2\n",
    "    leb = p.bl**2 / (p.sd / 10000.)**2 * v_box / (1.184 * 346.1**2)\n",
    "    cev = (p.sd / 10000.)**2 / p.bl**2 * 1.184 * l_over_a\n",
    "    \n",
    "    \n",
    "    omega = np.logspace(1.3, 4.3, 1000) * 2. * np.pi\n",
    "    zvc = p.le / 1000 * (omega * 1j)  # ** n_ = 1 anyway\n",
    "    re_ = p.re + zvc.real  # freq. dependent resistance\n",
    "    le_ = zvc.imag / omega  # freq. dependent inductance\n",
    "    \n",
    "    y_acoustic = -1j / (leb * omega - 1. / (omega * cev)) \n",
    "    z_mech = (1. / res + 1 / (omega * les * 1j) + omega * ces * 1j + y_acoustic)**(-1)\n",
    "    z_total = z_mech + re_ + 1j * omega * le_\n",
    "\n",
    "    transferfunc = 1j * (omega * z_mech / z_total) * re_ * ces\n",
    "    if l_over_a != np.inf:\n",
    "        transferfunc = transferfunc * (1j * omega * leb) / (1j * omega * leb + 1. / (1j * omega * cev))\n",
    "\n",
    "    freqs = omega / 2. / np.pi\n",
    "    imp_magnitude = np.abs(z_total)\n",
    "    imp_phase = np.angle(z_total) * 180. / np.pi\n",
    "    \n",
    "    efficiency = ((p.sd / 10000.)**2 * 1.184 / 346.1 / 2. / np.pi / p.re / ces**2 / p.bl**2) * np.abs(transferfunc)**2\n",
    "    power_spl = 112.1 + 10. * np.log10(efficiency)\n",
    "    \n",
    "    return {'omega': omega, 'power_spl': power_spl}\n",
    "\n",
    "        \n",
    "\n",
    "def spl_response(speaker_params):\n",
    "    \"\"\" Return the simulated pressure response of a speaker,\n",
    "    given its parameters, the frequency space and the resolution of simulation steps.\n",
    "    \n",
    "    Pressure is proportional to driver velocity and frequency.\n",
    "    \"\"\"\n",
    "    \n",
    "    calculate_impedance()\n",
    "    \n",
    "   \n",
    "    \n",
    "def f_c(vas, v_box):\n",
    "    \"\"\" Calculate the resonance frequency in a sealed enclosure volume.\n",
    "    \"\"\"\n",
    "    return np.sqrt(vas / (v_box-1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qts: 1.26\n",
      "rms: None\n",
      "le: 0.84\n",
      "vd: None\n",
      "name: Eminence Alpha 15\n",
      "bl: 7.7\n",
      "sensitivity: 97.0\n",
      "mms: 59.0\n",
      "re: 5.88\n",
      "qes: 1.53\n",
      "fs: 41.0\n",
      "xmax: None\n",
      "vas: 260.0\n",
      "cms: 0.25\n",
      "qms: 7.23\n",
      "sd: 856.3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(20, 20000)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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NnTtXF154oSIjI/XFF19oz549Ov/88zVnzhyf80+cOFE33HCD3njjDV188cU6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\nFsUdNWqU1QTfcnxz585tUVwiImqKCT4RE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      "text/plain": [
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   "source": [
    "# test speaker setup\n",
    "fe103en = {\n",
    "    'name': 'Fostex FE103En',\n",
    "    'sensitivity': 89., \n",
    "    'sd': 50.,\n",
    "    'mms': 25.5,\n",
    "    'cms': 1.65,\n",
    "    'rms': None,\n",
    "    're': 7.5,\n",
    "    'le': 0.0398,\n",
    "    'bl': 4.99,\n",
    "    'vas': 5.95,\n",
    "    'fs': 83.,\n",
    "    'qts': 0.33,\n",
    "    'qes': 0.377,\n",
    "    'qms': 2.747,\n",
    "    'vd': None,\n",
    "    'xmax': 0.6\n",
    "}\n",
    "\n",
    "alpha15 = {\n",
    "    'name': 'Eminence Alpha 15',\n",
    "    'sensitivity': 97., \n",
    "    'sd': 856.3,\n",
    "    'mms': 59.,\n",
    "    'cms': 0.25,\n",
    "    're': 5.88,\n",
    "    'le': 0.84,\n",
    "    'bl': 7.7,\n",
    "    'vas': 260.,\n",
    "    'fs': 41.,\n",
    "    'qts': 1.26,\n",
    "    'qes': 1.53,\n",
    "    'qms': 7.23,\n",
    "    'rms': None\n",
    "}\n",
    "\n",
    "# alpha15, fe103en\n",
    "parameters = alpha15\n",
    "box_volume = 60. # liters\n",
    "port_area_by_length = 30. * 3. / 20. # area to length ratio of the port\n",
    "\n",
    "\n",
    "speaker = Speaker(parameters)\n",
    "# fill up missing parameters if possible\n",
    "# calc_params(speaker)\n",
    "\n",
    "print(speaker)\n",
    "\n",
    "r_ = calculate_impedance(speaker, v_box=box_volume, l_over_a=port_area_by_length)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax_power = fig.add_subplot(111, title=\"Speaker SPL\")\n",
    "ax_power.plot(r_['omega'] / 2. / np.pi, r_['power_spl'])\n",
    "ax_power.set_ylabel('SPL (dB 1W1m)')\n",
    "ax_power.set_xlabel('Frequency (Hz)')\n",
    "ax_power.set_xscale('log')\n",
    "ax_power.set_xlim([20, 20000])"
   ]
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