OK, so, tangentially related: I tried to do something once - I took small chunks of songs generated by SampleRNN in an attempt to stitch together the ones that sounded the most similar to create a much longer track.

The script [1] uses Essentia Chromaprint [2] to "grade" the similarity of audio tracks, and combine the ones with the closest chromaprint. No crossfade or BPM matching, just yolo concatenation.

I have a track on Soundcloud which uses the above technique (mashing together short generated clips by their chromagram), trained on Cannibal Corpse [3]

1: https://github.com/sevagh/1000sharks.xyz/blob/master/sampler...

2: https://essentia.upf.edu/reference/std_Chromaprinter.html

3: https://soundcloud.com/user-167126026/1000sharks-domainal-sk...

Interesting, I attempted to do the same as you but stopped just shy of BPM matching.

However I did get sound similarity working using an audio tagging neural net [1]. I chopped off the first and last 15 seconds of every song in my collection and ran them all through this analysis which produces a ~520 dimensional vector. I then targeted specific endings I wanted to match and used Euclidian distance to find the closest matching song beginning.

YMMV but I thought it actually worked pretty well, I just never got to automating the BPM matching. I can try to look for my old script if you're interested :)

[1] https://github.com/fschmid56/EfficientAT

my idea was to let an existing app like djay do the beat matching (which it does really well!) and demixing (less well), and then make a custom app that would act like a midi controller, adjusting the vocals, beats, melody while songs are transitioning, perhaps working off of cues bookmarked in the songs.