Music-JEPA learns a piano sound world model from actions

iScienceLuvr · x · 2026-07-27

• Music-JEPA proposes a world model for piano sound by treating music as an action-conditioned system.
• The model uses the audio as state and the pianoroll as action, and learns latent dynamics with a JEPA-style objective.
• The paper reports that the learned representation supports downstream tasks such as beat tracking, composer identification, and key estimation.
• It also enables piano transcription via planning: searching for actions that best explain a target sound.
• The attached paper image shows the model diagram, authors (including Yann LeCun), and the abstract framing the work as a self-supervised music world model.

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