Earlier quoted context omitted.
I can think of half a dozen ways to detect AI music in it's current form, btu I'm not sure anyone has actually bothered implementing such a system. Is anyone here aware of one? I might give it a go if not.
> 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.