Most retrieval and recommendation systems collapse melody, rhythm, and timbre into one monolithic score, making it hard to understand why two pieces are considered similar, let alone perform more nuanced searches.
In our latest work from AMAAI Lab at SUTD, we introduce MERIT (Music rEpresentations for dIsentangled similaRiTy) -- a framework that learns separate, interpretable representations for each of these three dimensions, built on top of a frozen foundation model.