语音信号处理 | Python实现端点检测_基于频带方差的端点检测的python

由于项目需要，我要使用Python对语音进行端点检测，在之前的博客使用短时能量和谱质心特征进行端点检测中，我使用MATLAB实现了一个语音端点检测算法，下面我将使用Python重新实现这个这个算法，并将其封装到VAD类中，如下是运行结果：

软件环境

Python3.8、scipy、pyaudio、matplotlib

程序

matlab程序转换到python还是挺容易的，VAD.py程序如下：

#!/usr/bin/python3
# -*- coding: utf-8 -*-

import numpy as np
import sys
from collections import deque
import matplotlib.pyplot as plt
import scipy.signal
import pyaudio
import struct as st

def ShortTimeEnergy(signal, windowLength, step):
    """
    计算短时能量
    Parameters
    ----------
    signal : 原始信号.
    windowLength : 帧长.
    step : 帧移.
    
    Returns
    -------
    E : 每一帧的能量.
    """
    signal = signal / np.max(signal) # 归一化
    curPos = 0
    L = len(signal)
    numOfFrames  = np.asarray(np.floor((L-windowLength)/step) + 1, dtype=int)
    E = np.zeros((numOfFrames, 1))
    for i in range(numOfFrames):
        window = signal[int(curPos):int(curPos+windowLength-1)];
        E[i] = (1/(windowLength)) * np.sum(np.abs(window**2));
        curPos = curPos + step;
    return E

def SpectralCentroid(signal,windowLength, step, fs):
    """
    计算谱质心
    Parameters
    ----------
    signal : 原始信号.
    windowLength : 帧长.
    step : 帧移.
    fs : 采样率.

    Returns
    -------
    C : 每一帧的谱质心.
    """
    signal = signal / np.max(signal) # 归一化
    curPos = 0
    L = len(signal)
    numOfFrames  = np.asarray(np.floor((L - windowLength) / step) + 1, dtype=int)
    H = np.hamming(windowLength)
    m = ((fs / (2 * windowLength)) * np.arange(1, windowLength, 1)).T
    C = np.zeros((numOfFrames, 1))
    for i in range(numOfFrames):
        window = H * (signal[int(curPos) : int(curPos + windowLength)])
        FFT = np.abs(np.fft.fft(window, 2 * int(windowLength)))
        FFT = FFT[1 : windowLength]
        FFT = FFT / np.max(FFT)
        C[i] = np.sum(m * FFT) / np.sum(FFT)
        if np.sum(window**2) < 0.010:
            C[i] = 0.0
        curPos = curPos + step;
    C = C / (fs/2)
    return C

def findMaxima(f, step):
    """
    寻找局部最大值
    Parameters
    ----------
    f : 输入序列.
    step : 搜寻窗长.

    Returns
    -------
    Maxima : 最大值索引 最大值
    countMaxima : 最大值的数量
    """
    ## STEP 1: 寻找最大值
    countMaxima = 0
    Maxima = []
    for i in range(len(f) - step - 1): # 对于序列中的每一个元素:
        if i >= step:
            if (np.mean(f[i - step : i]) < f[i]) and (np.mean(f[i + 1 : i + step + 1]) < f[i]): 
                # IF the current element is larger than its neighbors (2*step window)
                # --> keep maximum:
                countMaxima = countMaxima + 1
                Maxima.append([i, f[i]])
        else:
            if (np.mean(f[0 : i + 1]) <= f[i]) and (np.mean(f[i + 1 : i + step + 1]) < f[i]):
                # IF the current element is larger than its neighbors (2*step window)
                # --> keep maximum:
                countMaxima = countMaxima + 1
                Maxima.append([i, f[i]])

    ## STEP 2: 对最大值进行进一步处理
    MaximaNew = []
    countNewMaxima = 0
    i = 0
    while i < countMaxima:
        # get current maximum:
        
        curMaxima = Maxima[i][0]
        curMavVal = Maxima[i][1]

        tempMax = [Maxima[i][0]]
        tempVals = [Maxima[i][1]]
        i = i + 1

        # search for "neighbourh maxima":
        while (i < countMaxima) and (Maxima[i][0] - tempMax[len(tempMax) - 1] < step / 2):
            
            tempMax.append(Maxima[i][0])
            tempVals.append(Maxima[i][1])
            i = i + 1
            
        MM = np.max(tempVals)
        MI = np.argmax(tempVals) 
        if MM > 0.02 * np.mean(f): # if the current maximum is "large" enough:
            # keep the maximum of all maxima in the region:
            MaximaNew.append([tempMax[MI], f[tempMax[MI]]])
            countNewMaxima = countNewMaxima + 1   # add maxima
    Maxima = MaximaNew
    countMaxima = countNewMaxima
    
    return Maxima, countMaxima

def VAD(signal, fs):
    win = 0.05
    step = 0.05
    Eor = ShortTimeEnergy(signal, int(win * fs), int(step * fs));
    Cor = SpectralCentroid(signal, int(win * fs), int(step * fs), fs);
    E = scipy.signal.medfilt(Eor[:, 0], 5)
    E = scipy.signal.medfilt(E, 5)
    C = scipy.signal.medfilt(Cor[:, 0], 5)
    C = scipy.signal.medfilt(C, 5)
    
    E_mean = np.mean(E);
    Z_mean = np.mean(C);
    Weight = 100 # 阈值估计的参数
    # 寻找短时能量的阈值
    Hist = np.histogram(E, bins=10) # 计算直方图
    HistE = Hist[0]
    X_E = Hist[1]
    MaximaE, countMaximaE = findMaxima(HistE, 3) # 寻找直方图的局部最大值
    if len(MaximaE) >= 2: # 如果找到了两个以上局部最大值
        T_E = (Weight*X_E[MaximaE[0][0]] + X_E[MaximaE[1][0]]) / (Weight + 1)
    else:
        T_E = E_mean / 2
    
    # 寻找谱质心的阈值
    Hist = np.histogram(C, bins=10)
    HistC = Hist[0]
    X_C = Hist[1]
    MaximaC, countMaximaC = findMaxima(HistC, 3)
    if len(MaximaC)>=2:
        T_C = (Weight*X_C[MaximaC[0][0]]+X_C[MaximaC[1][0]]) / (Weight+1)
    else:
        T_C = Z_mean / 2
    
    # 阈值判断
    Flags1 = (E>=T_E)
    Flags2 = (C>=T_C)
    flags = np.array(Flags1 & Flags2, dtype=int)
    
    ## 提取语音片段
    count = 1
    segments = []
    while count < len(flags): # 当还有未处理的帧时
        # 初始化
        curX = []
        countTemp = 1
        while ((flags[count - 1] == 1) and (count < len(flags))):
            if countTemp == 1: # 如果是该语音段的第一帧
                Limit1 = np.round((count-1)*step*fs)+1 # 设置该语音段的开始边界
                if Limit1 < 1:
                    Limit1 = 1
            count = count + 1 		# 计数器加一
            countTemp = countTemp + 1	# 当前语音段的计数器加一
            
        if countTemp > 1: # 如果当前循环中有语音段
            Limit2 = np.round((count - 1) * step * fs) # 设置该语音段的结束边界
            if Limit2 > len(signal):
                Limit2 = len(signal)
            # 将该语音段的首尾位置加入到segments的最后一行
            segments.append([int(Limit1), int(Limit2)])
        count = count + 1
        
    # 合并重叠的语音段
    for i in range(len(segments) - 1): # 对每一个语音段进行处理
        if segments[i][1] >= segments[i + 1][0]:
            segments[i][1] = segments[i + 1][1]
            segments[i + 1, :] = []
            i = 1

    return segments

if __name__ == "__main__":
    CHUNK = 1600
    FORMAT = pyaudio.paInt16
    CHANNELS = 1 # 通道数
    RATE = 16000 # 采样率
    RECORD_SECONDS = 3 # 时长
    p = pyaudio.PyAudio()
    stream = p.open(format=FORMAT,
                    channels=CHANNELS,
                    rate=RATE,
                    input=True,
                    frames_per_buffer=CHUNK)
    frames = [] # 音频缓存
    while True:
        data = stream.read(CHUNK)
        frames.append(data)
        if(len(frames) > RECORD_SECONDS * RATE / CHUNK):
            del frames[0]
        datas = b''
        for i in range(len(frames)):
            datas = datas + frames[i]
        if len(datas) == RECORD_SECONDS * RATE * 2:
            fmt = "<" + str(RECORD_SECONDS * RATE) + "h"
            signal = np.array(st.unpack(fmt, bytes(datas))) # 字节流转换为int16数组
            segments = VAD(signal, RATE) # 端点检测
            # 可视化
            index = 0
            for seg in segments:
                if index < seg[0]:
                    x = np.linspace(index, seg[0], seg[0] - index, endpoint=True, dtype=int)
                    y = signal[index:seg[0]]
                    plt.plot(x, y, 'g', alpha=1)
                x = np.linspace(seg[0], seg[1], seg[1] - seg[0], endpoint=True, dtype=int)
                y = signal[seg[0]:seg[1]]
                plt.plot(x, y, 'r', alpha=1)
                index = seg[1]            
            x = np.linspace(index, len(signal), len(signal) - index, endpoint=True, dtype=int)
            y = signal[index:len(signal)]
            plt.plot(x, y, 'g', alpha=1)
            plt.ylim((-32768, 32767))
            plt.show()
    
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运行结果

下面是语音“语音信号处理”的端点检测结果：