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add akko mode
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@Sg4Dylan
Sg4Dylan committed Aug 29, 2017
1 parent b623a32 commit 4c115ff
Showing 1 changed file with 87 additions and 45 deletions.
132 changes: 87 additions & 45 deletions EmiyaEngine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,8 @@ class EmiyaEngine:
output_file_path = ''
output_sr = 0
split_size = 500
# 时域处理模式=Akko 频域处理=None
mode="Akko"

def __init__(self, input=None, output=None, output_sr=96000, split_size=500, cpu_thread=None):
# 导入参数
Expand All @@ -60,6 +62,9 @@ def init_engine(self):
self.input_file_path, sr=None, mono=False)
print("Load signal complete. ChannelCount: %s SampleRate: %s Hz" % (
str(len(self.input_signal_array)), str(self.input_sr)))
# CAUTION
if self.mode == "Akko":
print("You are processing in AKKO mode.")

def process_leader(self):
# 左右声道
Expand Down Expand Up @@ -118,51 +123,88 @@ def process_core(self, signal_piece, index):
this_temp_block = np.array([()])
this_temp_count = 0
# 各分片运算
for i in range(this_div_count):
# 起始点
this_start_pos = i * self.proc_fft_length
this_end_pos = i * self.proc_fft_length + self.proc_fft_length
# 当前分片
this_proc_piece = signal_piece[this_start_pos:this_end_pos]
# 待修正尾部指示及尾部补齐长度
this_suffix_flag = False
this_suffix_length = 0
# 真尾部指示
this_tail_flag = False
if i == (this_div_count - 1):
this_tail_flag = True
# 尾部判定
if len(this_proc_piece) != 2048:
this_suffix_flag = True
# 尾部补齐及长度记录
while len(this_proc_piece) != 2048:
this_proc_piece = np.append(this_proc_piece, [0])
this_suffix_length += 1
# FFT
this_proc_piece_fft = np.fft.fft(this_proc_piece, self.proc_fft_length) / (self.proc_fft_length)
# 计算接续点及最大幅值
this_freq_thd, this_base_amp, this_threshold_point = self.find_threshold_point(this_proc_piece_fft*2)
# print("Max Amp -> %s Threshold point -> %s" % (this_base_freq, this_threshold_point))
# 加抖动
this_proc_piece_fft = self.generate_jitter(this_proc_piece_fft, this_freq_thd, this_base_amp, this_threshold_point)
# IFFT
this_proc_piece_ifft = np.fft.ifft(this_proc_piece_fft, n=self.proc_fft_length)
# 输出分片实部
this_proc_piece = this_proc_piece_ifft.real
# 计算最终输出长度 (尾部需要排除掉补零部分)
this_append_length = self.proc_fft_length
if this_suffix_flag:
this_append_length -= this_suffix_length
# 直接追加到目标输出返回数组
# this_output = np.append(this_output, this_proc_piece[0:this_append_length])
# 使用缓冲区再输出返回 (内存消耗和上边的方法差不多，但是能稍微抵消掉 numpy 对大数组拼接的问题)
this_temp_block = np.append(this_temp_block, this_proc_piece[0:this_append_length])
this_temp_count += 1
# 尾部判定追加
if this_temp_count > self.split_size or this_tail_flag:
this_output = np.append(this_output, this_temp_block)
this_temp_block = np.array([()])
this_temp_count = 0
if self.mode == "Akko":
# 是否第一次执行
is_loop_once = True
# 前一次的数值
pre_value = 0
# 前一次操作的数值
pre_opt = 0
# Akko 系数: 直接影响频谱图的美观程度
sv_l = 0.02
sv_h = 0.55
# 实际操作
for i in range(this_whole_length):
# 构造抖动值
this_value = signal_piece[i]
linear_jitter = 0
if pre_value < this_value:
linear_jitter = random.uniform(this_value*-sv_l, this_value*sv_h)
else:
linear_jitter = random.uniform(this_value*sv_h, this_value*-sv_l)
# 应用抖动
if pre_opt*linear_jitter > 0:
signal_piece[i] = this_value + linear_jitter
elif pre_opt*linear_jitter < 0:
signal_piece[i] = this_value - linear_jitter
else:
pass
# 第一次操作特殊化处理
if is_loop_once:
linear_jitter = random.uniform(this_value*-sv_h, this_value*sv_h)
signal_piece[i] = this_value + linear_jitter
is_loop_once = False
# 保存到上一次记录
pre_value = this_value
pre_opt = linear_jitter

this_output = signal_piece
else:
for i in range(this_div_count):
# 起始点
this_start_pos = i * self.proc_fft_length
this_end_pos = i * self.proc_fft_length + self.proc_fft_length
# 当前分片
this_proc_piece = signal_piece[this_start_pos:this_end_pos]
# 待修正尾部指示及尾部补齐长度
this_suffix_flag = False
this_suffix_length = 0
# 真尾部指示
this_tail_flag = False
if i == (this_div_count - 1):
this_tail_flag = True
# 尾部判定
if len(this_proc_piece) != 2048:
this_suffix_flag = True
# 尾部补齐及长度记录
while len(this_proc_piece) != 2048:
this_proc_piece = np.append(this_proc_piece, [0])
this_suffix_length += 1
# FFT
this_proc_piece_fft = np.fft.fft(this_proc_piece, self.proc_fft_length) / (self.proc_fft_length)
# 计算接续点及最大幅值
this_freq_thd, this_base_amp, this_threshold_point = self.find_threshold_point(this_proc_piece_fft*2)
# print("Max Amp -> %s Threshold point -> %s" % (this_base_freq, this_threshold_point))
# 加抖动
this_proc_piece_fft = self.generate_jitter(this_proc_piece_fft, this_freq_thd, this_base_amp, this_threshold_point)
# IFFT
this_proc_piece_ifft = np.fft.ifft(this_proc_piece_fft, n=self.proc_fft_length)
# 输出分片实部
this_proc_piece = this_proc_piece_ifft.real
# 计算最终输出长度 (尾部需要排除掉补零部分)
this_append_length = self.proc_fft_length
if this_suffix_flag:
this_append_length -= this_suffix_length
# 直接追加到目标输出返回数组
# this_output = np.append(this_output, this_proc_piece[0:this_append_length])
# 使用缓冲区再输出返回 (内存消耗和上边的方法差不多，但是能稍微抵消掉 numpy 对大数组拼接的问题)
this_temp_block = np.append(this_temp_block, this_proc_piece[0:this_append_length])
this_temp_count += 1
# 尾部判定追加
if this_temp_count > self.split_size or this_tail_flag:
this_output = np.append(this_output, this_temp_block)
this_temp_block = np.array([()])
this_temp_count = 0

return [this_output, index]

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