nips nips2001 nips2001-179 nips2001-179-reference knowledge-graph by maker-knowledge-mining
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Author: Ali Taylan Cemgil, Bert Kappen
Abstract: We present a probabilistic generative model for timing deviations in expressive music. performance. The structure of the proposed model is equivalent to a switching state space model. 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 inferences are carried out using sequential Monte Carlo integration (particle filtering) techniques. For this purpose, we have derived a novel Viterbi algorithm for Rao-Blackwellized particle filters, where a subset of the hidden variables is integrated out. The resulting model is suitable for realtime tempo tracking and transcription and hence useful in a number of music applications such as adaptive automatic accompaniment and score typesetting. 1