Intelligent Feature Selection (with SVM or PCA)


That’s super interesting in terms of how useful it appears to be (like, in a concrete/usable sense), but also in terms of what descriptors end up making the most sense (at the end). Some of it is somewhat intuitive, as you point out, like loudness/pitch, but then MFCC13 is a funky one to randomly be in there. I guess that individual MFCCs would have quite a bit of variance given any arbitrary corpus (and potentially not others). It’s also potentially interesting for an LPT-type approach where what makes up some significance for each overall vector may not be intuitive.

Thanks for sharing the code too. I wonder how implementable something like that would be in a fully fluid.context~, although the PCA-based version approximates things quite well, and that would be manageable to implement given the current (native) tools.

Now that we’ve got a(n easy to use) visualizer for Max it will make some of these investigations easier. Like even testing the kmeans stuff, which I’ve not done much (any?) of on my own sounds.

Would also be interesting to run things on the classes I had made when trying to do this process here manually a while back (soo tedious…).