super amazing demo performance being able separate out music voice and background noises. do you have to explicitly specify what type of noise to separate?
I hope we keep making progress in isolating tracks in music. I love listening to stems of my favorite songs, I find all sorts of neat parts I missed out on. Listening to isolated harmonies is cool too.
The problem of track isolation is sometimes underconstrained, and so any AI system that does this will probably invent "neat parts" for us to hear that weren't necessarily in the original recording. It feels like using super-resolution models to notice details about your great-grandma's wedding dress.
From the papers I've read, the stem separation models all seem to train off what seems like a fairly small dataset that doesn't have great instrument representation.
I wonder if you could assemble a big corpus of individual solo instruments, then permute a cacophonous mix of them. IIRC the main training dataset is comprised of a limited number of real songs. But I think a model trained on real songs might struggle with more "out there" harmonies and mixes.
Playing with the background I tried to Isolate just the espresso machine and the train sounds in one of their demos and it seemed to fail. Maybe not the desired use case, but I thought it was odd that I could break it so easily on the sample material.
As someone recording myself playing music, I've been meaning to see if any of these tools are good enough yet to not only separate vocals from another instrument (acoustic guitar for example), but do so without any loss of fidelity (or least not a perceivable one).
The reason I'm interested in this is because recording with multiple microphones (one on guitar, one on the vocal), has it's own set of problems with phase relationship and bleed between the microphones, which causes issues when mixing.
Being able to capture a singing guitarist with a single microphone placed in just the right spot, but still being able to process the tracks individually (with EQ, compression, reverb, etc), could be really helpful.
I use moises frequently for track separation for learning songs. It does pretty dang well. I was shocked that the score of moises is ranked way worse than just about everything else, including lalal.ai, which I also used before buying moises. Perhaps lalal.ai has gotten better since I last tried it.
Maybe I'm totally misinterpreting, but the chart I'm looking at says "Net Win Rate of SAM Audio vs. SoTA Separation (text prompted)", so perhaps a lower number means that the alternative model is better?
Now that I go back and read it again I agree with you. Presumably "win rate" means what percent of the time did the SAM model (Meta's new one) beat the other tool over some set of examples.
Would be interesting to leverage the non spoken/environment noises to guide what level of detail and style of speech a chatbot replied with, for instance being more casual, gentle, with a touch more detail if in a quiet home/office environment, but more curt and concise with emphasized diction if the person is traveling, such as in a noisy train concourse.
People tend to do that subconsciously but bots ignorantly wittering on can be annoying and hard to use because they miss the cues.
This is incredible! I wouldn't have thought it was possible to cleanly separate tracks like that. I wonder to what extent the model is filling in gaps, akin to Samsung's "ultra zoom" moon.
I tried it with their examples, trying to isolate speech only or background music only, but it seems to produce audio that is near identical to the original.
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I wonder if you could assemble a big corpus of individual solo instruments, then permute a cacophonous mix of them. IIRC the main training dataset is comprised of a limited number of real songs. But I think a model trained on real songs might struggle with more "out there" harmonies and mixes.
The reason I'm interested in this is because recording with multiple microphones (one on guitar, one on the vocal), has it's own set of problems with phase relationship and bleed between the microphones, which causes issues when mixing.
Being able to capture a singing guitarist with a single microphone placed in just the right spot, but still being able to process the tracks individually (with EQ, compression, reverb, etc), could be really helpful.
- This feature is awesome for sample-based music
- Sample music is not what it was due to difficulties related to legal rights
- This model was probably created by not giving a damn about said rights
It's not clear from the blog post, the git page, and most other places if this will run on, even in big-O:
* CPU
* 16GB GPU
* 240GB server (of the type most business can afford)
* Meta/Google/Open AI/Anthropic-style data center