(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,)))
return numpy.log(feat)
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 samplerate of the signal we are working with.
    :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.
Compute log Mel-filterbank energy features from an audio signal.
Compute log Mel - filterbank energy features from an audio signal .
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,))):
  """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 samplerate of the signal we are working with.
    :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.
    """
  feat, energy = fbank(signal, samplerate, winlen, winstep, nfilt, nfft,
                       lowfreq, highfreq, preemph, winfunc)
  return numpy.log(feat)
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 )