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However, algorithms (and ML in particular) absolutely do have a role to play in music creation. What's broken is the general approach of statistical mimicry, e.g. raw deep learning. To generate good music programmatically, you need an algorithmic model of what makes music good.
If you understand what makes music good with a sufficient level of clarity, you can express it in rules form, and seek to algorithmically maximize this greatness factor.
As usual with AI, this requires first understanding the subject matter by yourself, instead of blindly throwing a large dataset at a large model -- an approach which could only ever achieve local interpolation. Find the model, don't just fit a curve.
The real question is if "greatness factor" is even well defined
In section 4 of the draft on how to find "minimum entropy" with an HMM I discuss music technologist, Cárthach Ó Nuanáin and his the dissertation "Connecting Time and Timbre: Computational Methods for Generative Rhythmic Loops in Symbolic and Signal Domains" using DAGS and HMMs.
So much of what matters even just to categorize existing music is cultural (learned their from data science ppl at Spotify). Music that is entirely culturally separate can have a shockingly similar sonic signature (Eg US country and Vietnamese folk music).
And so much of what makes music meaningful is its cultural, social and historical context. Not just its aesthetic beauty. Think bob Dylan or punk music.
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