Some sounds OK, most sounds like a series of plausible notes that nevertheless don't actually go anywhere. Most generative models tend to do OK at predicting the immediate next note, say, but they can't keep a macro-level structure unless you force that as a constraint. For me what has worked best is generating music and then re-interpreting that with some manual post-processing to get it into a less jarring form. A fairly recent project that is close to the state of the art: https://openai.com/blog/jukebox/ One of my own experiments: https://datasciencecastnet.home.blog/2021/05/13/whistlegen-g...

Really? My initial reaction is to disagree, though I do think I understand your point.

While the medium is music is purely physical, and physical things can be abstracted to mathematics, what do you think about the generation/composition of music. Most music isn't really composed of instruments playing single notes; it's composed of things like phrases phrases and expressions. The math is just an obtuse way of representing the phrases, emotions, and expressions.

What do you think of this perspective?

The result of that is what is played up and down the charts, learned from what sells 'records', and embedded into almost anything new to be more and more refined to the taste of the majority/masses. Also the stuff playing in supermarkets to make you buy more.