code: def dirWavFeatureExtractionNoAveraging(dirName, mt_win, mt_step, st_win, st_step):
      
  
      all_mt_feats = numpy.array([])
      signal_idx = numpy.array([])
      process_times = []
  
      types = ('*.wav', '*.aif',  '*.aiff', '*.ogg')
      wav_file_list = []
      for files in types:
          wav_file_list.extend(glob.glob(os.path.join(dirName, files)))
  
      wav_file_list = sorted(wav_file_list)
  
      for i, wavFile in enumerate(wav_file_list):
          [fs, x] = audioBasicIO.readAudioFile(wavFile)
          if isinstance(x, int):
              continue        
          
          x = audioBasicIO.stereo2mono(x)
          [mt_term_feats, _, _] = mtFeatureExtraction(x, fs, round(mt_win * fs),
                                                      round(mt_step * fs),
                                                      round(fs * st_win),
                                                      round(fs * st_step))
  
          mt_term_feats = numpy.transpose(mt_term_feats)
          if len(all_mt_feats) == 0:                # append feature vector
              all_mt_feats = mt_term_feats
              signal_idx = numpy.zeros((mt_term_feats.shape[0], ))
          else:
              all_mt_feats = numpy.vstack((all_mt_feats, mt_term_feats))
              signal_idx = numpy.append(signal_idx, i * numpy.ones((mt_term_feats.shape[0], )))
  
      return (all_mt_feats, signal_idx, wav_file_list)
docs: This function extracts the mid-term features of the WAVE
      files of a particular folder without averaging each file.
  
      ARGUMENTS:
          - dirName:          the path of the WAVE directory
          - mt_win, mt_step:    mid-term window and step (in seconds)
          - st_win, st_step:    short-term window and step (in seconds)
      RETURNS:
          - X:                A feature matrix
          - Y:                A matrix of file labels
          - filenames: