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Comment by mwcampbell | Hacker News Reader

For your listening pleasure, here's a full-length demo. I decided to use the Jonathan Coulton classic "Re Your Brains", because I can legally share and modify his music under its Creative Commons license.

First, the original:

https://mwcampbell.us/tmp/spleeter-demo/jonathan-coulton-re-...

Now the derived stems:

Vocals: https://mwcampbell.us/tmp/spleeter-demo/jonathan-coulton-re-...

Accompaniment: https://mwcampbell.us/tmp/spleeter-demo/jonathan-coulton-re-...

Note: I'm not affiliated with this project or Mr. Coulton. I just think this is a cool project and wanted to share.

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Wow, I've listened to several attempts at this over the years, but this one is waaay better than anything I've heard. It's almost perfect.

IME, this tool is certainly an order of magnitude closer to the Holy Grail than anything I've ever heard. Kudos to Deezer R&D.

https://www.youtube.com/watch?v=KPlmrq_rAzQ

Holly cow! The separation is sort of perfect! Thanks for the demo.

"Indistinguishable from magic."

Did it just work or did you have to supply something beyond the original JC track?

Just worked.

They need to link these on the project readme as a demo.

While it's a great technology, the result sounds somewhat robotic. On the original recording the voice sounds soft, but after separation it sounds like it is synthesized or passed through a vocoder, something is missing. The voice contains pieces of strumming sound. Guitar also sounds "blurred", as if someone cut an object from the picture and blurred the cut to make it less visible. Clap sound is distorted, on the original recording it sounds the same, but after separation it sounds different every time, as if it was filtered or compressed with low bitrate.

It is amazing how the ear manages to distinguish all the sounds without distortion.

It's easy to pick up on lots of little things, but this is still extremely impressive. It's also more than good enough for people who want to practice singing/playing over the song by themselves.

I think the next step would be to train a network that can un-robotify songs and then run it on this.

> On the original recording the voice sounds soft, but after separation it sounds like it is synthesized or passed through a vocoder

funny, the original recording seemed kind of robotic to me! maybe not robotic, but like it's been filtered somehow. but that might just be my not-so-great headphones

The original lead vocal is definitely processed. You can hear that processing clearly if you apply the classic vocal removal (really, center removal) effect. (My favorite implementation of that is the Center Cut DSP for foobar2000.)

That roboticness is because there's overlap in frequencies between the voice and the instruments.

I have no idea how this tool splits them up at the implementation level but I imagine it tries to split it up based on frequencies and when it lifts out the voice, it's cutting out a ton of frequencies that would normally be in your voice so now it sounds very unnatural, blocky and metallic.

With studio quality headphones I can notice a massive difference for the worse between the original and separated vocal track. It reminds me of when I turned on a noise gate too high when recording audio for my courses. That noise gate clamps down on certain frequencies to help eliminate room noise but it also dampens or removes natural frequencies that occur on the low end of an average male voice. It gives that same very jagged sounding audio waveform.

I don't argue that it's a great technology, but some of the neighbour commenters wrote things like "perfect", and to me it doesn't sound like "perfect" yet.

For example, in the beginning, if you listen to the phrase "from the office ...", in original recording it sounds smooth, like a single phrase (and the voice is warm and pleasant), but in the separated track it sounds like it is synthesized from pieces that are not properly connected, like vocaloid songs. It sounds little harsher. Transitions sound unnatural. And the phrase "heya Tom" in the separated track is split into "heya" and "Tom" with some unnatural sound between (or maybe in the beginning of "Tom"). It is like transitions you can hear in vocaloid tracks. Or the kind of artifacts you get if you over-compress an MP3 file.

And "it's good to see you" part also sounds robotic.

Maybe it's losing some of harmonics, but in a different way for different syllables and they don't sound like a single phrase anymore.

It’s perfect relative to the most wildly optimistic expectations anyone could have reasonably held beforehand.

The vocals don’t have any significant residual artefacts from drum hits or any residual bleed-through of instruments playing the same notes. It’s magical.

> While it's a great technology, the result sounds somewhat robotic.

That's like complaining about how bad the pig plays the violin. This is absolutely incredible. The complexity level for this problem is right off the scale and the software does a passable job of it. Given some time and more training data and a few more people working on it this has serious potential.

I think you meant how well the pig paints. [1] [2]

[1] https://pigcasso.org/wp-content/uploads/2018/12/7.jpg [2] Also, I am not affiliated with anything in particular that has been mentioned, or with the pig.

I think the original was in relation to pigs dancing.

this is like someone just flew you to the surface of Mars in an hour and your only comment is that the ride was bumpy. The demo above is mind blowing.

I basically want to run this over every Steve Gadd recording I have.

People are hard at work on denoising of audio using neural networks also. I would expect that if one trained denoisers for each type of source separated here, and passed the separated audio through them, things would get even better.

spleeter is using neural networks.

Yes, for source separation. Denoising is generally a separate task. A denoising network would take in a noisy signal for a single source and outputs a cleaned up one. It would be trained on the specific source type, for example vocals.

Gotcha, thanks.

Maybe the source was compressed audio instead of flac/wav?

Edit: the source is an mp3, which removes audio frequencies based on perception/masking with other frequencies. It's perfectly normal that it is showing artifacts. A better source is needed.

I encoded the source in MP3 for your convenience. But the source that I fed into Spleeter was FLAC.

Edit: I uploaded the original FLAC to my web server last night, before I decided that MP3 would be more convenient:

https://mwcampbell.us/tmp/spleeter-demo/jonathan-coulton-re-...

So Jonathan Coulton is now the new Suzanne Vega?

> So Jonathan Coulton is now the new Suzanne Vega?

Bravo. For people who didn't get the sublime reference, Suzanne Vega's song Tom's Diner was a benchmark test during development of the MP3.[1]

[1]: https://en.wikipedia.org/wiki/Tom%27s_Diner#The_"Mother_of_t...

The reference is even more apt because the two of them have collaborated. She sang the led vocal on his song "Now I Am an Arsonist" (from the album Artificial Heart).

Tom's Diner wasn't just a benchmark. Brandenburg listened to it obsessively, over and over, to the exclusion of a lot of other content he might have done well to pay more attention to. Which is what tends to happen when you work on audio processing code, for better or worse.

So that's why the MP3 format mangles male vocals so badly at all but the highest bitrates. Now you know the Rest Of The Story... or at least you've read it on the Internet.

This sounds really interesting. Is there a good writeup/oral history about this?

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