Predictive Music Modeling

When a person is listening to a song, she is anticipating, at any given moment, the timing and nature of the next event by decoding the musical signal. Even when analyzing a simple song, the brain utilizes complex correlations between the musical elements to make accurate predictions. Musical signals are richly patterned, with long-term dependencies, dependencies across time-scales, and correlations between parallel information streams; the melody depends on the rhythm, the rhythmic patterns depend on the form, and the intonation of the pitch depends on the placement within the phrase. The goal of this NSF CAREER project is to develop machine- learning (ML) models for predicting temporally structured events in the context of music, which take advantage of these complex correlations, and to use these models to help explain human musical expectation.