code: def mfcc(
          signal,
          sampling_frequency,
          frame_length=0.020,
          frame_stride=0.01,
          num_cepstral=13,
          num_filters=40,
          fft_length=512,
          low_frequency=0,
          high_frequency=None,
          dc_elimination=True):
      
      feature, energy = mfe(signal, sampling_frequency=sampling_frequency,
                            frame_length=frame_length, frame_stride=frame_stride,
                            num_filters=num_filters, fft_length=fft_length,
                            low_frequency=low_frequency,
                            high_frequency=high_frequency)
      if len(feature) == 0:
          return np.empty((0, num_cepstral))
      feature = np.log(feature)
      feature = dct(feature, type=2, axis=-1, norm='ortho')[:, :num_cepstral]
  
      # replace first cepstral coefficient with log of frame energy for DC
      # elimination.
      if dc_elimination:
          feature[:, 0] = np.log(energy)
      return feature
docs: Compute MFCC features from an audio signal.
  
      Args:
  
           signal (array): the audio signal from which to compute features.
               Should be an N x 1 array
           sampling_frequency (int): the sampling frequency of the signal
               we are working with.
           frame_length (float): the length of each frame in seconds.
               Default is 0.020s
           frame_stride (float): the step between successive frames in seconds.
               Default is 0.02s (means no overlap)
           num_filters (int): the number of filters in the filterbank,
               default 40.
           fft_length (int): number of FFT points. Default is 512.
           low_frequency (float): lowest band edge of mel filters.
               In Hz, default is 0.
           high_frequency (float): highest band edge of mel filters.
               In Hz, default is samplerate/2
           num_cepstral (int): Number of cepstral coefficients.
           dc_elimination (bool): hIf the first dc component should
               be eliminated or not.
  
      Returns:
          array: A numpy array of size (num_frames x num_cepstral) containing mfcc features.