Billboard Hot 100 Spectrograms Dataset and Self-Supervised Analysis

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I've been dabbling with a hobby project over the last month. I took all the songs from the Billboard Hot 100 from 1960-2020, converted them to spectrograms using the ssqueezepy library (thanks to u/OverLordGoldDragon for his great work), and trained a simple Resnet-style model to extract features using Contrastive Learning. Using the extracted features I did some basic analysis and clustering. In my write-up, I've provided Spotify links to representative songs from each cluster. I'm certainly not a music expert so I'm curious to hear what sorts of features you think the model extracted.


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