Earlier quoted context omitted.
> I can think of half a dozen ways to detect AI music in it's current form Can you give a few examples? For example, how to detect that the song I linked is AI compared to say, anything Taylor Swift produces, or to any overly produced pop song or an electronic beat.
My first instincts offhand were: * N-gram analysis of lyrics. Even good LLM's still exhibit some weird pattern's when analyzed at the n-gram level. * Entropy - Something like KL divergence maybe? There are a lot of ways to calculate entropy that can be informative. I would expect human music to display higher entropy. * Plain old FFT. I suspect you'll find weird statistical anomalies. * Fancy waveform analysis tricks…
This will likely have a lot of false positives on a lot of genres. E.g. I suspect genres like synthpop and trance (and a lot of other electronic music) will likely hit a lot of those points with regards to music and sampling.
Lyrics are also not a given (when they are likely curated by humans). E.g. compare the song I referenced (https://dumpstergrooves.bandcamp.com/track/he-talked-a-big-g...) to, say, Taylor Swift's current most listened to song: https://genius.com/Taylor-swift-the-fate-of-ophelia-lyrics I'd chose the AI one in a heart beat :)
I wonder if a combination of all of those may work for a subset of songs, but I don't think you can do it with any confidence :(