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Using an adaptive threshold might help you: calculating the mean amplitude over a small moving time window (e.g. 0.5 sec), and setting a threshold as a function of the mean (e.g. 3X). This should detect sudden changes in amplitude (which drum strokes almost always are).


My guess is that the currently popular and new and likely robust way to solve this detection problem is to feed a sequence of audio fingerprints (such as MFCCs) to an RNN machine learning algorithm that was trained on a large wide range of rhythm tracks mixed with increasing levels of realistic background noise. Feeding audio stream samples directly to a ...

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