“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,” Chordia explained. “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 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.

The project builds on Chordia's previous research, which focused on understanding musical creativity from cognitive and computational perspectives and was funded by a creativeIT grant from the NSF. More generally, the project is an outgrowth of his research in creating algorithms that can interpret and generate music to enhance and enable human creativity. An example is LaDiDa, a top-ten music iPhone app, that automatically composes music in response to solo singing and was commercialized from research in Chordia's lab.