code: def speed_feature(signal, sampling_rate, window, step):
      signal = np.double(signal)
      signal = signal / (2.0**15)
      signal = dc_normalize(signal)
  
      num_samples = len(signal)  # total number of signals
      cur_p = 0
      count_fr = 0
  
      lowfreq = 133.33
      linsc = 200 / 3.0
      logsc = 1.0711703
      nlinfil = 13
      nlogfil = 27
      n_mfcc_feats = 13
      nfil = nlinfil + nlogfil
      num_fft = window / 2
      if sampling_rate < 8000:
          nlogfil = 5
          nfil = nlinfil + nlogfil
          num_fft = window / 2
  
      # compute filter banks for mfcc:
      fbank, freqs = mfcc_filter_banks(
          sampling_rate,
          num_fft,
          lowfreq,
          linsc,
          logsc,
          nlinfil,
          nlogfil,
      )
  
      n_time_spectral_feats = 8
      n_harmonic_feats = 1
      n_total_feats = (
          n_time_spectral_feats + n_mfcc_feats + n_harmonic_feats
      )
      # st_features = np.array([], dtype=np.float64)
      st_features = []
  
      while cur_p + window - 1 < num_samples:
          count_fr += 1
          x = signal[cur_p : cur_p + window]
          cur_p = cur_p + step
          fft_magnitude = abs(fft(x))
          fft_magnitude = fft_magnitude[0:num_fft]
          fft_magnitude = fft_magnitude / len(fft_magnitude)
          Ex = 0.0
          El = 0.0
          fft_magnitude[0:4] = 0
          #        M = np.round(0.016 * fs) - 1
          #        R = np.correlate(frame, frame, mode='full')
          st_features.append(harmonic(x, sampling_rate))
      #        for i in range(len(X)):
      # if (i < (len(X) / 8)) and (i > (len(X)/40)):
      #    Ex += X[i]*X[i]
      # El += X[i]*X[i]
      #        st_features.append(Ex / El)
      #        st_features.append(np.argmax(X))
      #        if curFV[n_time_spectral_feats+n_mfcc_feats+1]>0:
      #            print curFV[n_time_spectral_feats+n_mfcc_feats],
      #            curFV[n_time_
      #            spectral_feats+n_mfcc_feats+1]
      return np.array(st_features)
  
imports: from __future__ import print_function
  import math
  import numpy
  import sys
  from scipy.fftpack import fft
  import matplotlib.pyplot
  from scipy.signal import lfilter
  from scipy.fftpack.realtransforms import dct
  from tqdm import tqdm
label_desc: Use image node_id 16 for calling a global function with example usage: speed_feature(signal, sampling_rate, window, step) and returns: np