Zero Crossing Rate及python实现

过零率（Zero Crossing Rate，ZCR）是指在每帧中，语音信号通过零点（从正变为负或从负变为正）的次数。这个特征已在语音识别和音乐信息检索领域得到广泛使用，是对敲击的声音的分类的关键特征。

特性：

(1).一般而言，清音（unvoiced sound）和环境噪音的ZCR都大于浊音（voiced sound）；
(2).由于清音和环境噪音的ZCR大小相近，因而不能够通过ZCR来区分它们；
(3).在实际当中，过零率经常与短时能量特性相结合来进行端点检测，尤其是ZCR用来检测清音的起止点；
(4).有时也可以用ZCR来进行粗略的基频估算，但这是非常不可靠的，除非有后续的修正（refine）处理过程。

一般情况下，过零率越大频率近似越高

import math
import wave
import numpy as np
import pylab as pl
def ZeroCR(waveData,frameSize,overLap):
    wlen = len(waveData)
    step = frameSize - overLap
    frameNum = math.ceil(wlen/step)
    zcr = np.zeros((frameNum,1))
    for i in range(frameNum):
        curFrame = waveData[np.arange(i*step,min(i*step+frameSize,wlen))]
        #To avoid DC bias, usually we need to perform mean subtraction on each frame
        #ref: http://neural.cs.nthu.edu.tw/jang/books/audiosignalprocessing/basicFeatureZeroCrossingRate.asp
        curFrame = curFrame - np.mean(curFrame) # zero-justified
        zcr[i] = sum(curFrame[0:-1]*curFrame[1::]<=0)
    return zcr

# ============ test the algorithm =============
# read wave file and get parameters.
fw = wave.open('21.wav','rb')
params = fw.getparams()
print(params)
nchannels, sampwidth, framerate, nframes = params[:4]
str_data = fw.readframes(nframes)
wave_data = np.fromstring(str_data, dtype=np.short)
wave_data.shape = -1, 1
#wave_data = wave_data.T
fw.close()

# calculate Zero Cross Rate
frameSize = 256
overLap = 0
zcr = ZeroCR(wave_data,frameSize,overLap)

# plot the wave
time = np.arange(0, len(wave_data)) * (1.0 / framerate)
time2 = np.arange(0, len(zcr)) * (len(wave_data)/len(zcr) / framerate)
pl.subplot(211)
pl.plot(time, wave_data)
pl.ylabel("Amplitude")
pl.subplot(212)
pl.plot(time2, zcr)
pl.ylabel("ZCR")
pl.xlabel("time (seconds)")
pl.show()

这里写图片描述

参看文章：
http://ibillxia.github.io/blog/2013/05/15/audio-signal-processing-time-domain-ZeroCR-python-realization/

Zero Crossing Rate及python实现

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