The GTZAN latent trajectory shows a completely different dynamics. The first two components explain 59.2% of the variance, which is a 20% drop compared to the speech datasets. Instead of sticking to tight clusters, the path fills the entire plane. This higher dimensionality shows that the model is learning how to capture the texture of tone, harmony and rhythm, and helps JEPA-v0 to get a competitive score of 0.481 for music captioning.
Fixable Headphones for Musicians
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