Neural Networks with Keras Cookbook

How to do it...

The strategy discussed above is coded as follows (the code file is available as Genre_classification.ipynb in GitHub):

1. Download the dataset and import the relevant packages:

import sys, re, numpy as np, pandas as pd, music21, IPython, pickle, librosa, librosa.dsiplay, osfrom glob import globfrom tqdm import tqdmfrom keras.utils import np_utils

2. Loop through the audio files to extract the  mel spectrogram input features of the input audio, and store the output genre for the audio input:

song_specs=[]genres = []for genre in os.listdir('...'): # Path to genres folder  song_folder = '...' # Path to songs folder  for song in os.listdir(song_folder):    if song.endswith('.au'): signal, sr = librosa.load(os.path.join(song_folder, song), ...