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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).


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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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