Variational Inference with Bayes Net Fragments for Beat Tracking and Rhythm Recognition

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• Charles Fox
• Friday 01 June 2007, 15:30-17:00
• Engineering Department, Baker Building, Division F meeting room, 5th floor.

If you have a question about this talk, please contact Taylan Cemgil.

It is useful for music perception and automated accompaniment systems to perceive a music stream as a series of bars containing beats and rhythm patterns. We present a method combining variational Bayesian inference in network fragments with a blackboard 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 some lower-level component is available to detect and classify onsets. Model posteriors provide principled model competition, and the system may be seen as providing a Bayesian interpretation of agent-based and blackboard systems.

This talk is part of the Audio and Music Processing (AMP) Reading Group series.

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