Modeling Form for On-line Following of Musical Performances

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 20

Markov Decision Processes and Uncertainty

Automated musical accompaniment of human performers often requires an agent be able to follow a musical score with similar facility to that of a human performer. Systems described in the literature represent musical scores in a way that assumes no large-scale structural variation of the piece during performance. If the performer deviates from the expected path by skipping or repeating a section, the system may become lost. We describe a way to automatically generate a Markov model from a written score that models the score form, and an on-line algorithm to align a performance to a score. The resulting system can follow performances that take alternate paths through the score without losing its place. We compare the performance of our system to that of sequence-based score followers on a melodic corpus of 98 Jazz melodies. Results show that explicitly representing the branching structure of a score significantly improves score following when the branch a performer may take is unknown beforehand.