Using FFT to build our first classifier

Nevertheless, we can now create some kind of musical fingerprint of a song using FFT. If we do this 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.

Increasing experimentation agility

Before we dive into the classifier training, let us first spend some time 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 the 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 for each file will be the same each time we run the classifier. We could therefore cache it and read the cached FFT representation instead of the wave file. We do this with the create_fft() function, which in turn uses scipy.fft() to create the FFT. For the sake of simplicity ...