I am looking for algorithm(s) to solve the following problem: Given a noisy .wav sound capture (some wind + friction noise on the microphone), how to detect the BPM of a soft drum beat?
I have attempted googling the subject, but the results are quite poor, due to high amount of mp3 related software for both analysis and fingerprint id generation. None of them supply information about how to actually do it.
I am aware of algorithms to remove the noise, but that still leaves me with the problem of detecting BPM. And depending on how the BPM problem is solved, it's possible that I don't even need to denoise (since drum tends to be in the lower frequencies and noise in higher, a simple low-pass might be sufficient pre-processing).
Answer
One method that works if there's a relatively strong drum beat is to take the magnitude of the STFT of the waveform, and then auto-correlate it in only the time dimension. The peak of the auto-correlation function will be the beat, or a submultiple of it.
This is equivalent to breaking up the signal into a lot of different frequency bands, finding the amplitude envelope of each, autocorrelating each envelope, and then summing them. The noise and other parts of music are averaged out by the cross-correlation operation.
This is because drum beats produce short-lived sound at many frequencies (vertical lines), while other parts of music are long-lived at only a few frequencies (horizontal lines), and noise is long-lived but random at all frequencies. You can see the beat repetition if you look at an STFT:
I came up with this for a school project to find a single BPM value for entire music files, but it could be adapted to a stream of audio with changing BPM, too. You'd need to process chunks that are at least twice as long as the period of the BPM you're looking for.
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