DOI:
Abstract:
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.