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Comment by adzm | Hacker News Reader

A great example of this is changing music into an image and using that to train and generate new images that get converted back into music. It was surprisingly successful. I think this approach is still used by the current music generators.

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The current music generators use next token prediction, like LLMs, not image denoising.

[0] https://arxiv.org/abs/2503.08638 (grep for "audio token")

[1] https://arxiv.org/abs/2306.05284

You are talking about piano roll notation, I think. While it's 2d data, it's not quite the same as actual image data. E.g., 2d conv and pooling operations are useless for music. The patterns and dependencies are too subtle to be captured by spatial filters.

I've seen this approach applied to spectrograms. Convolutions do make enough sense there.

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.

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...

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.

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