Yeah I know what you mean. The "sweet spot" on some synths. Just don't know how to pose that as a supervised learning problem, or what it would do (limit the ra

Yeah I know what you mean. The "sweet spot" on some synths. Just don't know how to pose that as a supervised learning problem, or what it would do (limit the range of parameters?) I'm not sure the amount of parameters in normal substractive synth+filter+mod architecture is large enough for very interesting results that you couldn't achieve otherwise, or how you would generate a training set that can produce meaningful mapping. I also think wavenet sample by sample generation and interpolation between latent features doesn't sound that exciting, as cool as it technically is. We'll find some place to use machine learning in music/audio eventually :) I think perhaps more natural sounding pitch shifting could be one area (since you could learn the structure of sound of different instruments at various pitches), reverb removal, denoising, polyphonic audio to midi - things like that, where you have obvious training data.