Nevertheless, we can now create some kind of musical fingerprint of a song using FFT. If we do that for a couple of songs and manually assign their corresponding genres as labels, we have the training data that we can feed into our first classifier.
Before we dive into the classifier training, let us first spend some thoughts on experimentation agility. Although we have the word "fast" in FFT, it is much slower than the creation of the features in our text-based chapters. And because we are still in an experimentation phase, we might want to think about how we could speed up the whole feature creation process.
Of course, the creation of the FFT per file will be the same each time we are running the classifier. We could, therefore, cache it and read the cached FFT representation instead of the complete WAV file. We do this with the create_fft() function, which, in turn, uses scipy.fft() to create the FFT. For the sake...