Tempo tracking and rhythm quantization by sequential Monte Carlo

We present a probabilistic generative model for timing deviations in expressive music. The structure of the proposed model is equivalent to a switching state space model. We formu(cid:173) late two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as fil(cid:173) tering and maximum a posteriori (MAP) state estimation tasks. The inferences are carried out using sequential Monte Carlo in(cid:173) tegration (particle filtering) techniques. For this purpose, we have derived a novel Viterbi algorithm for Rao-Blackwellized particle fil(cid:173) ters, where a subset of the hidden variables is integrated out.