[c3534l]

I'm not always getting the greatest results. Mixing Portishead with Perturbator gave me sensical results, but Perturbator with Dance With the Dead should have given me other synthwave/outrun style faux-80s music. Instead it gave me The Notorious BIG, Sound Garden, and the Foo Fighters.

[tahw]

Huh! I also asked for something weird (Frank Zappa + Animal Collective) and got Foo Fighters. I wonder if there's some vector math going on that makes Foo Fighters crop up if the input songs are too orthogonal.

[warpri81]

You are probably correct about the vector math! We learn low level embeddings from playlists we scraped from Spotify (with little discretion) and are still working on the algorithm.

We are essentially trying to draw a line between the embeddings of the two songs and find "close" songs to points along that line. This should give us a somewhat smooth transition - at least that's the hope.

I suspect some of it may be that we are using a euclidean line through the vector space, but using cosine distance for similarity. We're still trying to get the hang of using the vectors to build a smooth transition between songs.

We are also tuning our model and training variables, as well as pulling in more playlists, which should help (I hope).

[c3534l]

I don't think that's what's going on. Perturbator and Dance with the Dead are basically interchangeable since synthwave music is basically just a bunch of contemporary John Carpenter knock-offs.

[warpri81]

Thanks for checking it out!

We used thousands of Spotify playlists to train on, but obviously more popular genres are going to be heavily represented. For rarer songs, it may not pick up on the association between two songs.

We are still trying to tune our heuristics and scrape more playlists, which should help it to learn rarer songs better.