Measuring Context Use in Music Generation

How can we measure whether a musical continuation carries forward one player’s part and relates to the other players? We introduce Predictive Musical Information (PMus), a framework grounded in information theory for evaluating a continuation’s relationship to its shared context and individual players. PMus uses an autoregressive model as an evaluator to compare a fixed continuation’s predictability when parts of its past context are provided, withheld, or replaced. It accommodates the single mixed output common to music generators, without requiring separate tracks for each player. With an external evaluator, PMus can assess human performances and outputs from open and closed source generators without access to the generating model’s parameters or probabilities. We evaluate five measures across four open music generation models through controlled experiments and a listening study with 85 musically trained participants. The context contrasts agree with musicians’ choices on 62–67% of judgments, with the shared-context measure PMus-Duo highest at 66–67%, whereas ranking by predictability under the true past alone agrees below chance on the same judgments. The measures also respond differently to controlled changes in what the generator was given. Because listeners answered the two questions alike, the perceptual separation of self-continuation from partner response remains open. PMus provides a practical foundation for evaluating how generated music relates to its context.