A mixed graphical model for rhythmic parsing

  • Authors:

  • Christopher Raphael

  • Affiliations:

  • Department of Mathematics and Statistics, University of Massachusetts, Amherst

  • Venue:
  • UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
  • Year:
  • 2001

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Abstract

A method is presented for the rhythmic parsing problem: Given a sequence of observed musical note onset times, we simultaneously estimate the corresponding notated rhythm and tempo process. A graphical model is developed that represents the evolution of tempo aud rhythm and relates these hidden quantities to an observable performance. The rhythm variables are discrete and the tempo and observation variables are continuous. We show how to compute the globally most likely configuration of the tempo and rhythm variables given an observation of note onset times. Preliminary experiments are presented on a small data set. A generalization to computing MAP estimates for arbitrary conditional Gaussian distributions is outlined.