(samples,
                  sr=8000,
                  winlen=0.025,
                  winstep=0.01,
                  feature_size=40)
return np.stack([feat, delta, _delta_delta], axis=-1)
extract logfbank with delta and delta-delta
  Return:
      ndarray of shape [nfrmae, feature_size * 3]
extract logfbank with delta and delta-delta
  Return:
      ndarray of shape [nfrmae, feature_size * 3]
extract logfbank with delta and delta - delta Return : ndarray of shape [ nfrmae feature_size * 3 ]
def extract_fbank(samples,
                  sr=8000,
                  winlen=0.025,
                  winstep=0.01,
                  feature_size=40):
  ''' extract logfbank with delta and delta-delta
  Return:
      ndarray of shape [nfrmae, feature_size * 3]
  '''
  feat = psf.logfbank(
      samples,
      nfilt=feature_size,
      samplerate=sr,
      winlen=winlen,
      winstep=winstep,
      lowfreq=0,
      highfreq=None,
      preemph=0.97)
  delta = psf.delta(feat, N=2)
  _delta_delta = psf.delta(delta, N=2)
  return np.stack([feat, delta, _delta_delta], axis=-1)
def extract_fbank ( samples , sr = 8000 , winlen = 0.025 , winstep = 0.01 , feature_size = 40 ) : feat = psf . logfbank ( samples , nfilt = feature_size , samplerate = sr , winlen = winlen , winstep = winstep , lowfreq = 0 , highfreq = None , preemph = 0.97 ) delta = psf . delta ( feat , N = 2 ) _delta_delta = psf . delta ( delta , N = 2 ) return np . stack ( [ feat , delta , _delta_delta ] , axis = - 1 )