Can the Computer Learn to Play Music Expressively?
I will discuss my recent work in creating a computer program that plays the role of a sensitive musical accompanist in a non-improvisatory piece for soloist and accompaniment. An accompanist must synthesize a number of different sources of information: The accompanist must follow the presciption of the musical score, must “listen to” the soloist, and must learn from rehearsals, all while obeying an internal sense of musicality. During live performance, my accompaniment system combines these sources of information into to a coherent probabilistic model, a Bayesian belief network, from which it can deduce the optimal course of action in real time. I will provide a demonstration. Christopher Raphael is currently an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts, Amherst where his interests include Bayesian belief networks and hidden Markov models and their applications to various recognition problems. Prior to his current appointment
