[zozbot234]

Are you aware of the system I linked above? D.D. Johnson has a blogpost https://www.danieldjohnson.com/2015/08/03/composing-music-wi... with plenty of examples of what an instance of his model can generate. It may not be all that "good" in an absolute sense, but it's at least musically interesting, the opposite of elevator music. (There's also a proprietary model/AI called AIVA about which very little is known, but it does seem to be bona-fide AI output - albeit released in versions that have been orchestrated by humans - based on what it sounds like.)

[gnulinux]

Yes I'm familiar and...

> Here's a taste of things to come

it sounds like randomly generated MIDI... Doesn't sound like anything to me at all.

Music is very subjective but I've so far seen no model that's convincing. If you like it that's cool I suppose. I personally use algorithmic composing plenty in my own compositions (I write music for piano) and these kind of models don't do it for me. They're definitely tools, you can use them like ChatGPT to get a sense of things but we're decades away from producing "music" this way imho.

[zozbot234]

Well yes, this model like others is quite far from giving you a finished piece. But if it's giving you "a sense" of possibly useful ideas, that's enough to make it more than "random". (Besides, I'm not sure that we would even want AI to produce music on its own with zero human input - what would be the point of that? So "just noodling around, giving you some ideas to get started with" is quite good as far as it goes.)

[gnulinux]

My point is that -- in my experience and musical taste -- deterministic algorithms (e.g. literal scripts you write yourself to generate MusicXML, MIDI, lilypond, PDF etc) are orders of magnitude more useful than these neural network ML models that give you monolithic chunks of music. You can still use NN models in your scripts (e.g. have a model to determine chord distance, tonality etc) but there is no universal musical model that has come remotely close to convincing me so far. Of course, when it comes to Western classical music, "counterpoint" is probably the closest you can get to a universal musical model that you'll attempt to find in all pieces of music, but even that comes nowhere near even remotely close to explaining great majority of musical statements (even in something as contrapuntal as Bach). Especially when we come to 20th and 21st century music when people actively react to this model.

[zozbot234]

Yes it depends on what you're trying to do. If you're looking for something to automate part of your composition and make it an "algorithmic" piece where the computer picks the notes, these models are just too limited for that, at least so far.

BTW, "counterpoint" generally refers to one facet of how Western music works, the process of setting "note against note" in musical lines (or "voices") that preserve some kind of autonomy. But there's many other things that explain what makes music sound good, both within a single line and on a broader view, where music is written to target "rest points" or "key areas", and repeat or develop "thematic" material.

(The model I pointed to above doesn't even try in the least to explore these broader-scale things, it's trained on a very small-scale view of its input. It deals pretty well with counterpoint, and the inner workings of a single line. It ends up doing interesting things nonetheless when trying to make sense of its music as it randomly drifts out of the established scale - ISTM that it sometimes ends up changing key as a result of alterations in melody, not always in the background harmony. One could see this as a kind of very light and subtle atonality, even as part of what's clearly a 'tonal' style. It also knows about different historical styles within tonal music, and manages to overlay and transition between them quite well IMHO.)