code: def ssc(
      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 Spectral Subband Centroid 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.
      """
      highfreq = highfreq or samplerate / 2
      signal = sigproc.preemphasis(signal, preemph)
      frames = sigproc.framesig(
          signal, winlen * samplerate, winstep * samplerate, winfunc
      )
      pspec = sigproc.powspec(frames, nfft)
      pspec = numpy.where(
          pspec == 0, numpy.finfo(float).eps, pspec
      )  # if things are all zeros we get problems
  
      fb = get_filterbanks(nfilt, nfft, samplerate, lowfreq, highfreq)
      feat = numpy.dot(pspec, fb.T)  # compute the filterbank energies
      R = numpy.tile(
          numpy.linspace(1, samplerate / 2, numpy.size(pspec, 1)),
          (numpy.size(pspec, 0), 1),
      )
  
      return numpy.dot(pspec * R, fb.T) / feat
  
file_path: python_speech_features/python_speech_features/base.py
imports: from __future__ import division
  import numpy
  from python_speech_features import sigproc
  from scipy.fftpack import dct
label_desc: Use image node_id 5 for calling a global function with example usage: ssc(signal, samplerate, winlen, winstep, nfilt, nfft, lowfreq, highfreq, preemph, winfunc) and returns: unknown