code: def delta(feat, N):
      
      if N < 1:
          raise ValueError('N must be an integer >= 1')
      NUMFRAMES = len(feat)
      denominator = 2 * sum([i**2 for i in range(1, N+1)])
      delta_feat = numpy.empty_like(feat)
      padded = numpy.pad(feat, ((N, N), (0, 0)), mode='edge')   # padded version of feat
      for t in range(NUMFRAMES):
          delta_feat[t] = numpy.dot(numpy.arange(-N, N+1), padded[t : t+2*N+1]) / denominator   # [t : t+2*N+1] == [(N+t)-N : (N+t)+N+1]
      return delta_feat
docs: Compute delta features from a feature vector sequence.
  
      :param feat: A numpy array of size (NUMFRAMES by number of features) containing features. Each row holds 1 feature vector.
      :param N: For each frame, calculate delta features based on preceding and following N frames
      :returns: A numpy array of size (NUMFRAMES by number of features) containing delta features. Each row holds 1 delta feature vector.