Surprised there's no discussion of FFT, power spectra, etc. Would like to see someone with an electrical engineering/signal processing background work on this problem.
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.