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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 Montel 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.
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