for real musicwe can quickly see that the fft looks not as beautiful as in the preceding toy exampleusing fft to build our first classifier neverthelesswe can now create some kind of musical fingerprint of song using fft if we do this for couple of songsand manually assign their corresponding genres as labelswe have the training data that we can feed into our first classifier increasing experimentation agility before we dive into the classifier traininglet us first spend some time on experimentation agility although we have the word "fastin fftit is much slower than the creation of the features in our text-based and because we are still in the experimentation phasewe might want to think about how we could speed up the whole feature-creation process of coursethe 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(functionwhich in turn uses scipy fft(to create the fft for the sake of simplicity (and speed!)let us fix the number of fft components to the first , in this example with our current knowledgewe do not know whether these are the most important ones with regard to music genre classification--only that they show the highest intensities in the earlier fft example if we would later want to use more or less fft componentswe would of course have to recreate the cached fft files