Seems like MusicXML is a great format for ML applications. You need to start somewhere and machine-readable code is important.
Music generation from notation is pretty much the MINST toy-scale equivalent for sequence/language learning models, it's surprising that there's so little attention being paid to it despite how easy it to get started with.
I'm a hobbyist in this space (am a composer myself as well a software engineer) and currently all tools are very poor. MusicXML is better than MIDI. MEI [1] is better than MusicXML etc.
The problem is there is miniscule amount of effort and money spent into this field because music overall makes peanuts. It really doesn't justify training expensive ML algorithms which is unfortunate.
It depends what you're trying to do. If you're trying to generate sheet music that's pretty to look at and easily understandable to a performer, then yes obviously it's not enough. If you want notes that will actually sound good when played back, it's hard to beat it.
I strongly disagree with this. There is no good algorithmic music generator trained on MIDI. They all generate elevator music.
> 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.
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.)
This depends enormously on the instrument. Consider someone playing a piece live on a keyboard: we can keep a MIDI recording of that and we've captured everything about their performance that the audience hears.