梅尔频率倒谱系数（mfcc）及Python实现

import wave
import numpy as np
import math
import matplotlib.pyplot as plt
from scipy.fftpack import dct

def read(data_path):
 '''读取语音信号
 '''
 wavepath = data_path
 f = wave.open(wavepath,'rb')
 params = f.getparams()
 nchannels,sampwidth,framerate,nframes = params[:4] #声道数、量化位数、采样频率、采样点数
 str_data = f.readframes(nframes) #读取音频，字符串格式
 f.close()
 wavedata = np.fromstring(str_data,dtype = np.short) #将字符串转化为浮点型数据
 wavedata = wavedata * 1.0 / (max(abs(wavedata))) #wave幅值归一化
 return wavedata,nframes,framerate

def enframe(data,win,inc):
 '''对语音数据进行分帧处理
 input:data(一维array):语音信号
   wlen(int):滑动窗长
   inc(int):窗口每次移动的长度
 output:f(二维array)每次滑动窗内的数据组成的二维array
 '''
 nx = len(data) #语音信号的长度
 try:
  nwin = len(win)
 except Exception as err:
  nwin = 1 
 if nwin == 1:
  wlen = win
 else:
  wlen = nwin
 nf = int(np.fix((nx - wlen) / inc) + 1) #窗口移动的次数
 f = np.zeros((nf,wlen)) #初始化二维数组
 indf = [inc * j for j in range(nf)]
 indf = (np.mat(indf)).T
 inds = np.mat(range(wlen))
 indf_tile = np.tile(indf,wlen)
 inds_tile = np.tile(inds,(nf,1))
 mix_tile = indf_tile + inds_tile
 f = np.zeros((nf,wlen))
 for i in range(nf):
  for j in range(wlen):
   f[i,j] = data[mix_tile[i,j]]
 return f

def point_check(wavedata,win,inc):
 '''语音信号端点检测
 input:wavedata(一维array)：原始语音信号
 output:StartPoint(int):起始端点
   EndPoint(int):终止端点
 '''
 #1.计算短时过零率
 FrameTemp1 = enframe(wavedata[0:-1],win,inc)
 FrameTemp2 = enframe(wavedata[1:],win,inc)
 signs = np.sign(np.multiply(FrameTemp1,FrameTemp2)) # 计算每一位与其相邻的数据是否异号，异号则过零
 signs = list(map(lambda x:[[i,0] [i>0] for i in x],signs))
 signs = list(map(lambda x:[[i,1] [i<0] for i in x], signs))
 diffs = np.sign(abs(FrameTemp1 - FrameTemp2)-0.01)
 diffs = list(map(lambda x:[[i,0] [i<0] for i in x], diffs))
 zcr = list((np.multiply(signs, diffs)).sum(axis = 1))
 #2.计算短时能量
 amp = list((abs(enframe(wavedata,win,inc))).sum(axis = 1))
# # 设置门限
# print('设置门限')
 ZcrLow = max([round(np.mean(zcr)*0.1),3])#过零率低门限
 ZcrHigh = max([round(max(zcr)*0.1),5])#过零率高门限
 AmpLow = min([min(amp)*10,np.mean(amp)*0.2,max(amp)*0.1])#能量低门限
 AmpHigh = max([min(amp)*10,np.mean(amp)*0.2,max(amp)*0.1])#能量高门限
 # 端点检测
 MaxSilence = 8 #最长语音间隙时间
 MinAudio = 16 #最短语音时间
 Status = 0 #状态0:静音段,1:过渡段,2:语音段,3:结束段
 HoldTime = 0 #语音持续时间
 SilenceTime = 0 #语音间隙时间
 print('开始端点检测')
 StartPoint = 0
 for n in range(len(zcr)):
  if Status ==0 or Status == 1:
   if amp[n] > AmpHigh or zcr[n] > ZcrHigh:
    StartPoint = n - HoldTime
    Status = 2
    HoldTime = HoldTime + 1
    SilenceTime = 0
   elif amp[n] > AmpLow or zcr[n] > ZcrLow:
    Status = 1
    HoldTime = HoldTime + 1
   else:
    Status = 0
    HoldTime = 0
  elif Status == 2:
   if amp[n] > AmpLow or zcr[n] > ZcrLow:
    HoldTime = HoldTime + 1
   else:
    SilenceTime = SilenceTime + 1
    if SilenceTime < MaxSilence:
     HoldTime = HoldTime + 1
    elif (HoldTime - SilenceTime) < MinAudio:
     Status = 0
     HoldTime = 0
     SilenceTime = 0
    else:
     Status = 3
  elif Status == 3:
   break
  if Status == 3:
   break
 HoldTime = HoldTime - SilenceTime
 EndPoint = StartPoint + HoldTime
 return FrameTemp1[StartPoint:EndPoint]


def mfcc(FrameK,framerate,win):
 '''提取mfcc参数 
 input:FrameK(二维array):二维分帧语音信号
   framerate:语音采样频率
   win:分帧窗长（FFT点数）
 output:
 '''
 #mel滤波器
 mel_bank,w2 = mel_filter(24,win,framerate,0,0.5)
 FrameK = FrameK.T
 #计算功率谱
 S = abs(np.fft.fft(FrameK,axis = 0)) ** 2
 #将功率谱通过滤波器
 P = np.dot(mel_bank,S[0:w2,:])
 #取对数
 logP = np.log(P)
 #计算DCT系数
# rDCT = 12
# cDCT = 24
# dctcoef = []
# for i in range(1,rDCT+1):
#  tmp = [np.cos((2*j+1)*i*math.pi*1.0/(2.0*cDCT)) for j in range(cDCT)]
#  dctcoef.append(tmp)
# #取对数后做余弦变换 
# D = np.dot(dctcoef,logP)
 num_ceps = 12
 D = dct(logP,type = 2,axis = 0,norm = 'ortho')[1:(num_ceps+1),:]
 return S,mel_bank,P,logP,D
 


def mel_filter(M,N,fs,l,h):
 '''mel滤波器
 input:M(int)：滤波器个数
   N(int)：FFT点数
   fs(int)：采样频率
   l(float)：低频系数
   h(float)：高频系数
 output:melbank(二维array):mel滤波器
 '''
 fl = fs * l #滤波器范围的最低频率
 fh = fs * h #滤波器范围的最高频率
 bl = 1125 * np.log(1 + fl / 700) #将频率转换为mel频率
 bh = 1125 * np.log(1 + fh /700) 
 B = bh - bl #频带宽度
 y = np.linspace(0,B,M+2) #将mel刻度等间距
 print('mel间隔',y)
 Fb = 700 * (np.exp(y / 1125) - 1) #将mel变为HZ
 print(Fb)
 w2 = int(N / 2 + 1)
 df = fs / N
 freq = [] #采样频率值
 for n in range(0,w2):
  freqs = int(n * df)
  freq.append(freqs)
 melbank = np.zeros((M,w2))
 print(freq)
 
 for k in range(1,M+1):
  f1 = Fb[k - 1]
  f2 = Fb[k + 1]
  f0 = Fb[k]
  n1 = np.floor(f1/df)
  n2 = np.floor(f2/df)
  n0 = np.floor(f0/df)
  for i in range(1,w2):
   if i >= n1 and i <= n0:
    melbank[k-1,i] = (i-n1)/(n0-n1)
   if i >= n0 and i <= n2:
    melbank[k-1,i] = (n2-i)/(n2-n0)
  plt.plot(freq,melbank[k-1,:])
 plt.show()
 return melbank,w2

if __name__ == '__main__':
 data_path = 'audio_data.wav'
 win = 256
 inc = 80
 wavedata,nframes,framerate = read(data_path)
 FrameK = point_check(wavedata,win,inc)
 S,mel_bank,P,logP,D = mfcc(FrameK,framerate,win)