International Computer Music Association.
In Proceedings of the International Computer Music Conference, pp.
Agon, C., Assayag, G., Fineberg, J., & Rueda, C. Statistical cooling: A general approach to combinatorial optimization problems. 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. Our simulation results suggest better results with sequential methods. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Exact 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 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. The continuous hidden variables denote the tempo. The switch variables correspond to discrete note locations as in a musical score. The structure of the proposed model is equivalent to a switching state space model. We present a probabilistic generative model for timing deviations in expressive music performance.
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