31–40 of 182 posts
Not only are the results good, but the music is generated decently rapidly. The implications are clear: whoever wants to make a quick fortune on YouTube should start converting and uploading truckloads of songs as fast as possible. The demand is there. I could easily see that bringing in millions of views.
I gave a talk at pycon this year about dsp [1], specifically some of the complexities surrounding this. I came across a few other ml projects that claimed to do this as well, and the biggest hold up is getting enough properly trained data, tagged appropriately, in order to let the models train correctly. in the git repo of this project they also explicitly state you need to train on your own data set, though you can…
I'm sure you've thought of this, but could/have the tracks from the Rock Band games be used for training? There are thousands of them and they're separated into different instrument tracks. They even had bands re-record songs sometimes where seperate masters couldn't be found. If I recall correctly, Third Eye Blind did this for Semi-Charmed Life.
Wait the implications of this are huge for electronic music DJs
I gave a talk at pycon this year about dsp [1], specifically some of the complexities surrounding this. I came across a few other ml projects that claimed to do this as well, and the biggest hold up is getting enough properly trained data, tagged appropriately, in order to let the models train correctly. in the git repo of this project they also explicitly state you need to train on your own data set, though you can…
But you probably don't need to bother with old recordings since there is SO MUCH music being produced via tracking software right now I feel like it should be possible to get a pretty big dataset - the difference being, of course, professional production that affects how all these things sound in the final mix.
Although... if you have enough songs with separated tracks, couldn't you just recombine tracks and adjust the settings to create a much, much broader base for training? Just a dozen songs could be shuffled around to give you a base of 10,000+ songs easily enough. That might lead to a somewhat brittle result but it would be a decent start.
Earlier quoted context omitted.
I'm sure you've thought of this, but could/have the tracks from the Rock Band games be used for training? There are thousands of them and they're separated into different instrument tracks. They even had bands re-record songs sometimes where seperate masters couldn't be found. If I recall correctly, Third Eye Blind did this for Semi-Charmed Life.
To add there is a format of music called Stems designed for DJ's and live remixers from Native Instruments which is a disassembly of the song into it's various parts. https://www.native-instruments.com/en/specials/stems/
Either way, an open source competitor to Melodyne is a welcome addition!
I gave a talk at pycon this year about dsp [1], specifically some of the complexities surrounding this. I came across a few other ml projects that claimed to do this as well, and the biggest hold up is getting enough properly trained data, tagged appropriately, in order to let the models train correctly. in the git repo of this project they also explicitly state you need to train on your own data set, though you can…
Seems like most music (from the 70s on at least) is recorded multi-track and the data is out there, just not accessible to anybody. If you ever watch Rick Beato videos, he takes classic songs and isolates vocal/drum/etc. tracks all the time, I'm not sure how he has access to them: https://www.youtube.com/playlist?list=PLW0NGgv1qnfzb1klL6Vw9... But you probably don't need to bother with old recordings since there is S…
I find that pretty amazing given the litigiousness of some in the music industry, but there we are.
Side note: I discovered Rick Beato a few months ago and I've watched heaps of his videos. It's really fascinating hearing old classics torn down to their constituent parts. Here's one of my favourites of his: https://www.youtube.com/watch?v=ynFNt4tgBJ0 (Boston - More than a feeling).