From avant-garde and experimental to soundtracks and commercial electronica, artistis in all kind of genres have used methods, libraries and tools for direct generation of waveforms, whether other processing will happen to them aftewards (manipulations, effects, and so) or they're the final result (there's also a big "generative music" scene as well, both academic and artistic). And that's for decades now. Of course recently hany have also started using AI's to produce generative music - with the API spitting out a final "waveform".
>Even karaoke machines use a signal pipeline to blend the singer's voice with the backing track. Generating finished waveforms is only good for elevator music.
Perhaps you have the kind of music played at the Grand Ole Orpy or something in mind.
Here are some trivial ways to use generated finished waveforms, sticking with the AI case alone:
- take the AI final result, sample it, and use it as you would loops from records or something like Splice.
- train the AI yourself, set parameters, tweek it, and the result is generative music you've produced (a genre that exists since the 60s at least, and is quite the opposite og "elevator music")
- use the generated music as a soundtrack for your film or video or video game
[0] https://github.com/acids-ircam/RAVE?tab=readme-ov-file
Also, I think artistic uses (such as Dadabots, who heavily used SampleRNN) show clearly that "musicians" like interesting tools, even if uncontrolled in some cases. Tools to exactly execute an idea are important (DAW-like), but so are novelty generating machines like (many) unconditional generators end up being. Jukebox is another nice example of this.
On the "good for elevator music" comment - the stuff I've heard from these models is rarely relaxing enough to be in any elevator I would ride. But there are snippets of inspiration in there for sure.
Generally, I do favor controllable models with lots of input knobs and conditioning for direct use, but there's space for many different approaches in pushing the research forward.
Different creators will work all kind of odd models into their workflows, even things that are objectively less "high quality", and not really controllable. To me, that's a great thing and reason enough to keep pushing unsupervised learning forward.
Pop, etymologically, can't be unpopular, and what's truly novel usually isn't popular, but I don't think it's true that what's popular can't be novel.