Ok, I think I’ve built it in Max with @weefuzzy’s help in the other thread.
I think I’ve done the maths right (do correct me if I’m wrong). I was a bit confused as to what I was getting out of the dump-ing process (a 21x21 vector in this case), but in the end I guess that’s how fluid.pca~ does its thing.
So I shove all the stuff into a coll then take out the amount of vectors I’m interested in. By default it does 5 (the amount of dimensions I asked for), but it can go higher as per @tedmoore’s suggestion. Interestingly the results lean much more towards the MFCCs when I do that.
Once that process is done, I do some peak picking to find the most important features (in this particular dataset).
I’ve not yet built anything that verifies and/or plays back sounds based on this, and honestly didn’t think I’d get this far with this today, but wanted to post my results as it’s really interesting.
A next step will be to try this on a much larger descriptor space (150+) to see what it makes of stats/derivatives, as this is my “lean and mean” 21D space (20(19) MFCCs + loudness/pitch), as well as adding something to test the accuracy.
pca.zip (350.6 KB)