PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Drum'n'Bayes: On-Line Variational Inference for Beat Tracking and Rhythm Recognition
Charles Fox, Iead Rezek and Stephen J. Roberts
In: roceedings of the the International Computer Music Conference(2007).

Abstract

It is useful for music perception and automated accompaniment systems to perceive a music stream as a series of bars containing beats. We present a proof-of-concept implementation of a Variational Bayesian (VB) system for simultaneous beat tracking and rhythm pattern recognition in the domain of semi-improvised music. This is music which consists mostly of known bar-long rhythm patterns in an improvised order, and with occasional unknown patterns. We assume that a lower-level component is available to detect and classify onsets. The system uses Bayesian network fragments representing individual bars and infers beat positions within them. Model posteriors provide principled model competition, and the system may be seen providing a Bayesian rationale for agent-based and blackboard systems. The psychological notion of priming is used to instantiate new candidate models.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
ID Code:3834
Deposited By:Iead Rezek
Deposited On:25 February 2008