Uncertainty in Artificial Intelligence
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A Mixed Graphical Model for Rhythmic Parsing
Christopher Raphael
Abstract:
A method is presented for the rhythmic parsing problem: Given a sequence of observed musical note onset times, we estimate the corresponding notated rhythm and tempo process. A graphical model is developed that represents the simultaneous evolution of tempo and rhythm and relates these hidden quantities to observations. 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 arbitrary conditional Gaussian distributions is outlined.
Keywords: conditional Gaussian distribution, MAP estimate, graphical models, rhythmic parsing,
Pages: 462-471
PS Link: http://fafner.math.umass.edu/papers/paper-298.ps
PDF Link: /papers/01/p462-raphael.pdf
BibTex:
@INPROCEEDINGS{Raphael01,
AUTHOR = "Christopher Raphael ",
TITLE = "A Mixed Graphical Model for Rhythmic Parsing",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "462--471"
}


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