code: def logfbank(signal,samplerate=16000,winlen=0.025,winstep=0.01,
               nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97,
               winfunc=lambda x:numpy.ones((x,))):
      
      feat,energy = fbank(signal,samplerate,winlen,winstep,nfilt,nfft,lowfreq,highfreq,preemph,winfunc)
      return numpy.log(feat)
docs: Compute log Mel-filterbank energy features from an audio signal.
  
      :param signal: the audio signal from which to compute features. Should be an N*1 array
      :param samplerate: the sample rate of the signal we are working with, in Hz.
      :param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
      :param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
      :param nfilt: the number of filters in the filterbank, default 26.
      :param nfft: the FFT size. Default is 512.
      :param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
      :param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
      :param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
      :param winfunc: the analysis window to apply to each frame. By default no window is applied. You can use numpy window functions here e.g. winfunc=numpy.hamming
      :returns: A numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector.