Facebook Research “Demucs” to separate mp3 into stems
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Hey all,
I thought I’d post about a recent tool that was actually created by Facebook that has the ability to accurately separate an mp3 into its stems. I’ve actually been using it a ton for just practicing drums in general, but obviously would work great in the context of what we’re all about here.
The code from their paper entitled: “Music Source Separation in the Waveform Domain” is here: https://github.com/facebookresearch/demucs
Included is the Python environment needed to take a MP3 and split it into stems for drums, bass, vocals, and “other” (often the guitar). It can do this with pretty remarkable accuracy in certain cases. I’ve posted an example from a Metallica song. You’ll see that sometimes you get some bleeding in the guitar/vocals, etc., but using this in combination with potentially the full song as the “backing track”, you could probably get something really high quality. I’ve tended to have better luck with bass + drums when it comes to the quality of the stems that are generated. Depending on the track all of the stems won’t have high quality, but figured I’d at least demonstrate an example here of what you can do with just a pretty simple install!
Here’s a link to the stems that were generated from Spit Out the Bone (Sorry, fixed the link here)
https://mega.nz/folder/fkpi2KqS#QxcyQ9F9mixkjcq1xGSzhQ
The stems download requires a decryption key.
Sorry about that, updated the link above and here it is with the key on the end:
https://mega.nz/folder/fkpi2KqS#QxcyQ9F9mixkjcq1xGSzhQ
Sounds similar to Spleeter, which also requires python and conda, except that it doesn’t separate piano. Would be interesting to compare results.
I’ve posted ezstems.com numerous times. Get with the times. It’s the same thing, only less hassle.
It’s actually not the same thing. It’s the same concept, but both have very different architectures under them (note: I am a data scientist by trade). Each probably have their advantages, but I’ve found Demucs to be better than Spleeter. Can see comparison among the numerous architectures here: https://ai.honu.io/papers/demucs/). However, I think in the context of Phase Shift, I’ll take the higher noted quality even if there is a little bit more bleeding. Though I’ll give you the ease of use of EZStems. Just offering another option out there
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