This project builds upon cosine.club - an amazing music similarity search engine posted the other day (https://news.ycombinator.com/item?id=40072062) which works by comparing audio embeddings between songs with this new model from essentia/upf https://essentia.upf.edu/models.html#discogs-effnet.
I wondered what these embeddings would look like when visualised (using UMAP). Would genre "clusters" pop up? Would similar genres appear next to each other in the embedding space? It turns out they do!
I've ended up using this a lot for discovering new music - there's something really useful about having discovery unbiased by song popularity - hope you enjoy!
Also if you're interested in how ai is impacting music beyond uninspired "song generators" we're working on a series of open-source ai/music experiments at https://vroomai.com/
Show HN: Field* – Explore a map of 1.8M songs based on audio similarity
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