(signal,
              samplerate=16000,
              winlen=0.025,
              winstep=0.01,
              nfft=512,
              lowfreq=0,
              highfreq=None,
              preemph=0.97,
              winfunc=lambda x: numpy.ones((x,)))
return pspec
Compute power spectorgram 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 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:  first is a numpy array of size (NUMFRAMES by nfft) containing power spectrogram.
Compute power spectorgram features from an audio signal.
Compute power spectorgram features from an audio signal .
def powerspec(signal,
              samplerate=16000,
              winlen=0.025,
              winstep=0.01,
              nfft=512,
              lowfreq=0,
              highfreq=None,
              preemph=0.97,
              winfunc=lambda x: numpy.ones((x,))):
  """Compute power spectorgram 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 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:  first is a numpy array of size (NUMFRAMES by nfft) containing power spectrogram.
    """
  highfreq = highfreq or samplerate / 2
  signal = sigproc.preemphasis(signal, preemph)
  frames = sigproc.framesig(signal, winlen * samplerate, winstep * samplerate,
                            winfunc)
  pspec = sigproc.powspec(frames, nfft)
  return pspec
def powerspec ( signal , samplerate = 16000 , winlen = 0.025 , winstep = 0.01 , nfft = 512 , lowfreq = 0 , highfreq = None , preemph = 0.97 , winfunc = lambda x : numpy . ones ( ( x , ) ) ) : highfreq = highfreq or samplerate / 2 signal = sigproc . preemphasis ( signal , preemph ) frames = sigproc . framesig ( signal , winlen * samplerate , winstep * samplerate , winfunc ) pspec = sigproc . powspec ( frames , nfft ) return pspec