University of Cambridge > Talks.cam > Audio and Music Processing (AMP) Reading Group > Variational Inference with Bayes Net Fragments for Beat Tracking and Rhythm Recognition

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

Add to your list(s) Download to your calendar using vCal

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.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2019 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity