语音波形，截断的频域输出以及语谱图制作

语音可视化

• 参考链接1
• 参考链接2
• 参考链接3

今天我想复现一下，文中语谱图提取部分的代码

由于输入的语音有单通道和双通道之分，处理方式是单通道不变，双通道只取一个通道的信息。附上代码：

import wave as weimport numpy as npimport matplotlib.pyplot as pltdef wavread(path):    wavfile =  we.open(path,"rb")    params = wavfile.getparams()    nchannels,samplewidth,framerate,nframes=params[:4]     datawav = wavfile.readframes(nframes)    wavfile.close()    wave_data = np.fromstring(datawav,dtype = np.short)    if nchannels==1: wave_data.shape=-1,1      if nchannels==2: wave_data.shape=-1,2    wave_data = wave_data.T    time = np.arange(0, nframes) * (1.0/framerate)    return wave_data[0],timepath = "1.wav"wavdata,wavtime = wavread(path)plt.plot(wavtime, wavdata,color = 'blue')plt.show()

得到如下的时域波形图

然后对原始语音信号处理，得到4k范围内的频率信号。为了理解操作过程，对fft变换的结果进行了总结：

def fft_4K(path):    # gain wav data    wavfile =  we.open(path,"rb")    params = wavfile.getparams()    nchannels,samplewidth,framerate,nframes=params[:4]     datawav = wavfile.readframes(nframes)    wavfile.close()    wave_data = np.fromstring(datawav,dtype = np.short)    if nchannels==1: wave_data.shape=-1,1      if nchannels==2: wave_data.shape=-1,2    wave_data = wave_data.T    # gain fft    df=framerate/(float)(nframes-1)      freq=[df*n for n in range(0,nframes)]      transformed=np.fft.fft(wave_data[0])      d=int(len(transformed)/2)      while freq[d]>4000:          d-=10      freq=freq[:d]      transformed=transformed[:d]      for i,data in enumerate(transformed):          transformed[i]=abs(data)      return freq, transformed

得到的结果

之后，为了进一步得到语谱图结果，采用如下代码，帧长为20ms，帧移为10ms。测试语音只保留3s内的信息，显示的频率范围是【0，7.5KHz】,之后的频率范围内的特征值被舍弃。

import numpy, waveimport numpy, matplotlib.pyplot as plt# target: gain spec from framename# input: filename, wav file path, string#        window_length_ms(/ms),window length(/ms), int#        window_shift_times(),rate of shit length, floatdef getSpectrum(filename, window_length_ms, window_shift_times):      # read data    wav_file = wave.open(filename, 'r')    params = wav_file.getparams()    # nchannels, channel number (like, 2 channel wav)    # sampwidth, sample percision rate (like, 2)    # framerate, sample rate, (like, 44100)    # wav_length, how much points after sampled, (int)    nchannels, sampwidth, framerate, wav_length = params[:4]    str_data = wav_file.readframes(wav_length)    wave_data = numpy.fromstring(str_data, dtype=numpy.short)    wav_file.close()    # gain log spectrogram    window_length = framerate * window_length_ms / 1000 # change time to points number    window_shift = int(window_length * window_shift_times) # change time to points number    nframe = (wav_length - (window_length - window_shift)) / window_shift # gain frame number    spec = numpy.zeros((window_length/2, nframe)) # store spectrogram [only half part]    for i in xrange(nframe):        start = i * window_shift        end = start + window_length        spec[:, i] = numpy.log(numpy.abs(numpy.fft.fft(wave_data[start:end])))[:window_length/2]    return spec# main processspeech_spectrum = getSpectrum('1.wav', 20, 0.5)  plt.imshow(speech_spectrum[:,:])plt.xlim(0, 300)plt.ylim(0, 150)plt.show()

得到的语谱图结果：