code: def extract_features(data, n_fft=2048):
      data = data.astype(np.float32)
  
      res = []
      for row in data:
          centroid = mfcc(row, n_fft=n_fft, sr=22050)
          res.append(
              [
                  np.min(centroid),
                  np.max(centroid),
                  np.median(centroid),
              ]
          )
  
      return np.array(res)
  
file_path: neupy/examples/rbfn/music_speech/train.py
imports: import numpy
  from neupy import algorithms
  from sklearn.utils import shuffle
  from sklearn import preprocessing, model_selection, metrics, decomposition
  import matplotlib.pyplot
  from librosa.feature import mfcc
  from getdata import train_test_data, parser
label_desc: Use image node_id 1 for calling a global function with example usage: extract_features(data, n_fft) and returns: np