These days roughly 20% of the songs coming through our platform for promotion are AI-generated. Roughly 75% of them are honest and declare their AI usage - but another 25% try to hide it. Some of them are actually writing scripts to "clean" their audio so that it can bypass detection.
'Detecting AI' is not a problem that has real solutions, the only avenue is something supply side like synthid. But that harms users too, by introducing further barriers for indie users.
This isn't like text classification, the signal many orders of magnitude higher bitrate and so many more corners need to be cut. It's likely going to be nearly impossible or at least not remotely worth it to generate an audio signal that is truly undetectable in the foreseeable future.
You are right, the output of a model that generates music directly is, for now, easy to categorize as AI.
What this big flux of AI generated music online isn't really that. It'a a tiny bit autogenerated stuff and a whole lot of automatically remixed stuff. The reason it can not be easily classified as AI is because quite a bit of human produced music is also that, and you'd just shut out real users.
Today. Trying to detect AI is like extracting water from puddles in a lake that is quickly drying up. What is the point in the short term if it's impractical in the long term? It will catch some low-hanging fruit in the best case, and will find false positives in the worst.
if the ai picked a bunch of samples and combined them together and mastered using an mcp to a DAW, how is that particularly distinguishable vs a person doing the same thing badly?
i can see how the llm generation pictures of spectrograms is essy to spot, but much less so with tool following.
even worse of you using a vla to have it actually play the guitar and use the recording as a sample.
theres some time and setup to make it happen sure, but somebody put that all in a studio and expose an mcp
Nobody open sourced their detection algorithm as that would just trigger a cat-and-mouse game between Suno/Udio and a detection platform (and Suno/Udio have way more VC money than you do), but plenty are being sold as a service and work very reliably.
It's trivial to vibe-code something that detects watermarked output and accidental model fingerprints. But next week the watermarks will be defeated, and the accidental fingerprints will change and ultimately disappear. It's not possible to generally solve the "To what degree is this audio AI generated?" problem, any more than it has been to solve the same problem for text and images. https://mitsloanedtech.mit.edu/ai/teach/ai-detectors-dont-wo...
The question I'm more interested in is why other music streaming services are not interested in doing this trivially easy work to get rid of spam, even if it's just for the short run as you assume it will be.
Honestly I hope that the AI filter would be much better in terms of false positive than the aforementioned one, if only because it should be easier via statistical methods.
Aside, your analogy doesn’t make sense. Horoscopes are generally not in the business of signal detection, and are usually enjoyed by the reader of the horoscope, like any other art. If you had used a sudoku solver your analogy would make a bit more sense.
Whether it's terrible music or not is somewhat irrelevant. Plagiarized music doesn't sound worse than the original.
What seems to matter more is the story behind the work. Basically, if the author is a grifter trying to make a quick buck, then it's slop. You can make an argument that Taylor Swift qualifies as slop, but most people will disagree. The public will be the final arbiter. All I really want is a big red lever to cast my vote.
I (and I think most poeple?) object to slop because it is objectively bad, not because it was generated.
And popularity is unrelated to quality.