Machine Learning for Drummers (opens in new tab)

> Are people taught that in schools or something? Because I personally can't classify those sounds, don't know these names, and I'm not sure how I was supposed to learn them, other by playing in a band.

This is something that is taught at schools with a music program. (Although, not necessarily discretely).

If you are someone whom has played music before, it is easy to forget what music sounded like before your ear was trained. (i.e. certain instruments and harmonies can be indistinguishable without training)

Is it common to have never played on at a drum kit in your entire life?

Just because you don't know the right names for the labels doesn't mean you're unable to label them.

A kick drum is the big foot operated one on the floor. Congrats you now know the name for the label and you can most definitely pick out a kick drum sample out from other samples.

Yes! While the technique I used in this post is pretty simplistic, you could use a similar method to solve what's generically known as the [speaker recognition problem](https://en.wikipedia.org/wiki/Speaker_recognition). That said, this is an open problem within audio research, and there are solutions much more complex than what I have here.

An example of one open source speaker recognition project is [bob.bio.spear](https://pypi.org/project/bob.bio.spear/), which I haven't tried, but looks promising.

I’m looking for something that can do this as well. Anything out already?

Hey! In order:

- I did consider using image processing techniques as opposed to decision trees, but the point here was not to come up with the most advanced and accurate classifier possible, but rather to build something simple and explainable to folks without an ML (or even a CA) background.

- I haven't tried this extensively on non-drum-like percussion, but that'd be a great follow up post.

Stock FFT is a really high-dimension feature vector given the number of training examples used here, and most of the resolution of the FFT would be unneeded anyways. "Average loudness in several frequency ranges" captures spectral information at a granularity much more appropriate to the data and classification task. For analyzing drum samples you don't need a lot of frequency resolution, although other low-dimension spectral features like MFCCs or flux would probably be useful.