We use a combo of modified mfcc and k-means at Mogees to get over 95% accuracy of percussion sounds.
To add a few words to what Jamie says, on what I've learned about working in this domain; 1) It really helps to understand your problem and the possible ways it can be assisted by ML. Is real-time training needed? Or do you have lots of off-line time for your ML to "think"? 2) How many training examples will you use, and where will they come from? How diverse/typical are they? 3) Will you supervise the ML (give clues to steer it) or allow it to make up its own mind about classes/clusters (unsupervised)? 4) What kind of output are you hoping for, prediction, regression a definite match, or a set of probabilities, or a vector of distances from possible matches? 5) How does signal time figure in your problem? Do you want pitch or duration independence? 6) How will you segment and arrange data relative to block boundaries and the size of any transform (fft/wavelet)? Tiny variations can lead to big differences. Will you zero pad to remove junk? Will you use windows/envelopes to soften edges? 7) What do you know about the source and structure of the sounds to be processed? Are they; i) 'Samples' with identical byte patterns? ii) Never heard before? iii) Known structure, segments, chunks. eg. speech? iv) Highly structured 'samples', hashable, eg. MIR v) Largely similar, seeking a specific structural variation? vi) Transient, sustained, harmonic? Or complex evolution? Machine learning right now is a set of quite specialised building blocks. Each of the above shapes of problem may suggest substantially different choices of components and configuration. Pre-processing, like shelf EQ and compression can make a _huge_ difference to the quality and reliability of results. I havent used the ml.lib before , but the idea of having a load of ML components to play with in Pd is really attractive and I'm sure you will have tons of fun! cheers, Andy
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