Monte Carlo Methods for Tempo Tracking and Rhythm Quantization

Monte Carlo Methods for Tempo Tracking and Rhythm Quantization (2003)

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by Ali Taylan Cemgil , Bert Kappen

Venue:	JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
Citations:	44 - 7 self

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Datum	Value	Source
TITLE	Monte Carlo Methods for Tempo Tracking and Rhythm Quantization	user correction
AUTHOR NAME	Ali Taylan Cemgil	user correction
AUTHOR AFFIL	Geert Grooteplein 21 cpkl- 231; CEMGILSNN.KUN.NL; BEITSNN.KUN.NL	user correction
AUTHOR ADDR	University of Nijmegen; NL 6525 EZ Nijmegen, The Netherlands	user correction
AUTHOR NAME	Bert Kappen	user correction
ABSTRACT	We present a probabilistic generarive model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Ex- act computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval.	user correction - Legacy Corrections
YEAR	2003	INFERENCE
VENUE	JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH	user correction
VENUE TYPE	JOURNAL	INFERENCE
PAGES	45--81	INFERENCE
VOLUME	18	INFERENCE
CITATIONS	47 found	ParsCit 1.0