I’ll respond in here first to avoid overlap in the other thread.
In my testing I found a big improvement going from 13MFCCs to 20MFCCs (with a drop again at 24MFCCs), but that was for the kind of drum stuff I was doing with short drum hits.
I’m curious of the variables with and without derivatives (I found I got good results without), but to see how this also works with some PCA in the mix. As much as I’d like to assume that if you whittle down the features pre-PCA that you can expect the output of PCA to be better than if you just fed everything into PCA first, I have to imagine that wouldn’t be the case. Since “you” and “the algorithm” are looking at different things.
I wrote down in my to-do list coming up with a thing that analyzes everything, and then runs iterative permutations to calculate what combinations of features pre/post PCA gives me the best matching, that’s kind of a nightmare to build in Max…
At minimum I’ll try doing something like in this patch where you analyze “everything” and then create sub-datasets with different permutations for easier testing (rather than my previous approach in which I’d create a different analysis file per features I wanted).