Pretty sure Deezer did a in depth article about how they detect and remove AI music. But it seems more likely they are detecting artifacts of the current tools, not something that would be impossible to bypass eventually.
I also remembered a friend telling me about someone talking about a method to detect AI music. I forget the specifics (I haven’t watched the video, was only told about it) but I remember the channel.
> 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.
* 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. AI's tend to do it in "chunks" I would expect the waveforms to have steeper/higher impulses and strange gaps. This probably explains why they still sound "off" to hifi fans.
* SNR analysis - Maybe a repeat of one of the above, but worth expanding on. The actual information density of the channel will be different because diffusion is basically compression.
* Subsampling and comparing to a known library. It's likely that you can identify substantial chunks that are sampled from other sources without modification - Harder because you need a library. Basically just Shazam.
* Consistency checks. Are all of the same note/instrument pairs actually generated by the same instrument throughout, or subtly different. Most humans won't notice, but it's probably easy to detect that it drifts (if it does).
That's just offhand though. I would need to experiment to see which if any actually work. I'm sure there are lots more ways.
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.
> 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 :(
Thats a solid point. Pretty much all of my ideas are probabilistic. I suspect you're right and it will have to work a bit like spam detection, where each "fail" for a test is seen as one indicator that adds to a score. Then above a threshold score it's flagged for further review and sent to a "spam" folder where a human can judge.
At scale no. I just meant that on Spotify I have been hearing music lately that sounds off to me like this and when I look it up sure enough it was AI.
E.g. how is this worse and needs to be removed: https://youtu.be/L3Uyfnp-jag?si=SL4Jc4qeEXVgUpeC but crap that top pop artists vomit out into the world doesn't