I am talking about using spectrograms (Fourier transform into frequency domain then plotted over time) that results in a 2d image of the song, which is then used to train something like stable diffusion (and actually using stable diffusion by some) to be able to generate these, which is then converted back into audio. Riffusion used this approach.
IF you think about it, a music sheet is just a graph of Fourier transform. It shows at any points of time, what frequency is present (the pitch of note), and for how long (duration of note),
it is no such thing. nobody maps overtones on sheet, durations are toast, you need to macroexpand all flat/sharps, volume is passed by vibe-words, it has 500+ of historical compost and so on. sheet music to fft is like wine tasting to a healthy meal
A spectrogram is lossy and not a one-to-one mapping of the waveform. Riffusion is, afaik, limited to five-second-clips. For these, structure and coherence over time isn't important and the data is strongly spatially correlated. E.g., adjacent to a blue pixel is another blue pixel. To the best of my knowledge no models synthesize whole songs from spectrograms.
How does Spotify “think” about songs when it is using its algos to find stuff I like?
Does it really need to think about the song contents? It can just cluster you with other people that listen to similar music and then propose music they listen to that you haven't heard.
That's one method they use, but "just cluster" is doing a lot of heavy lifting in that sentence. It's why Erik Bernhardsson came up with the Approximate Nearest Neighbors Oh Yeah algorithm (or ANNOY for short)
> We use it at Spotify for music recommendations. After running matrix factorization algorithms, every user/item can be represented as a vector in f-dimensional space. This library helps us search for similar users/items. We have many millions of tracks in a high-dimensional space, so memory usage is a prime concern.
[0] https://erikbern.com/2013/04/12/annoy.html
[1] https://github.com/spotify/annoy?tab=readme-ov-file
Yeah, I am obviously not a data scientist.
I guess where I was getting at is they do not technically even need to know genres to recommend songs. In practice though, they probably have to know them anyway for playlists, but I assume they can have the song owners provide that when the songs are uploaded, and artists specify it when they create their profile.
This article [0] investigates some of the feature extraction they do, so it's not just collaborative filtration.
[0]: https://www.music-tomorrow.com/blog/how-spotify-recommendati...