new_contents: 
  # coding: utf-8
  
  # In[8]:
  
  import os
  import glob
  import sys
  import numpy as np
  
  from sklearn.model_selection import train_test_split
  import keras
  import librosa
  
  import librosa.display
  import matplotlib.pyplot as plt
  import matplotlib.style as ms
  ms.use('seaborn-muted')
  get_ipython().magic('matplotlib inline')
  
  GENRE_DIR = "../data/songData/genres/"
  GENRE_LIST = []
  # GENRE_LIST.append("blues")
  # GENRE_LIST.append("classical")
  GENRE_LIST.append("country")
  GENRE_LIST.append("disco")
  GENRE_LIST.append("hiphop")
  GENRE_LIST.append("jazz")
  GENRE_LIST.append("metal")
  GENRE_LIST.append("pop")
  GENRE_LIST.append("reggae")
  GENRE_LIST.append("rock")
  
  
  # In[2]:
  
  # checking data shapes
  fn = "../data/songData/genres/blues/blues.00000.wav"
  y, sr = librosa.load(fn)
  
  mfcc = librosa.feature.mfcc(y=y, sr=sr,n_mfcc=30)
  y_harmonic, y_percussive = librosa.effects.hpss(y)
  percus_mfcc = librosa.feature.mfcc(y=y_percussive, sr=sr,n_mfcc=30)
  harmonic_mfcc = librosa.feature.mfcc(y=y_harmonic, sr=sr,n_mfcc=30)
  
  
  # In[3]:
  
  data = []
  data.append(mfcc)
  data.append(percus_mfcc)
  data.append(harmonic_mfcc)
  data = np.array(data)
  print(data.shape)
  
  
  # In[4]:
  
  def create_data_for_conv(genre_list=GENRE_LIST):
      os.chdir(GENRE_DIR)
      for genre in genre_list:
          glob_wav = os.path.join(genre, "*.wav")
          for fn in glob.glob(glob_wav):
              create_mfcc_3d(fn)
  
  
  # In[5]:
  
  def create_mfcc_3d(fn):
      y, sr = librosa.load(fn)
      y_harmonic, y_pertcussive = librosa.effects.hpss(y)
      mfcc = librosa.feature.mfcc(y=y, sr=sr,n_mfcc=30)
      harmonic_mfcc = librosa.feature.mfcc(y=y_harmonic, sr=sr,n_mfcc=30)
      percus_mfcc = librosa.feature.mfcc(y=y_pertcussive, sr=sr,n_mfcc=30)
      data = []
      data.append(mfcc)
      data.append(percus_mfcc)
      data.append(harmonic_mfcc)
      data = np.array(data)
      
      base_fn, ext = os.path.splitext(fn)
      data_fn = base_fn + ".mfcc3d"
      np.save(data_fn, data)
      print("Written", data_fn)
  
  
  # In[6]:
  
  import subprocess
  subprocess.run(['jupyter', 'nbconvert', '--to', 'python', 'create_dataset_mfcc_3d.ipynb'])
  
  
  # In[9]:
  
  create_data_for_conv()
  
  
subject: modify create dataset of mfcc 3d