I don't think your analysis of my comment is valid. :) Regardless of opinion on audio files, it's the case that many AI approaches to music have focused on sym

I don't think your analysis of my comment is valid. :) Regardless of opinion on audio files, it's the case that many AI approaches to music have focused on symbolic representations of music, which works, as do non-NN ways of procedurally generating music using the same symbols. What AI can theoretically add to a procedurally generated midi track is expressive performance, but that's not exactly its strength and probably needs better curation than just giving it lots of audio files. And even the "pure audio" approaches like OpenJukebox have to break out vocals and treat them separately (because it's something of an understatement to say that reflecting a static image is not a more complex problem than generating four minutes of singing with appropriate pitch, intonation and emotion and the impression of a consistent genre-appropriate vocalist), and the background music is... kind of muddy sounding and compositionally uneven. The main thrust of my argument wasn't the complexity of music though, the main thrust is comparative difficulty of compiling the training sets. More work to annotate and more licensing complications than just scooping up public domain images especially if you want a well rounded model. For another example take OpenAI's jukebox. It's clear the "Heavy Metal In The Style of Rage" vocals use a model trained on metal bands, but it's also clear that Rage Against The Machine wasn't prominent amongst them. Also probably the case there's simply not enough Rage Against The Machine albums to get your digital Zach de la Rocha vocals out of the uncanny valley even with the relevant permissions