Tempo Tracking and Rhythm Quantization by Sequential Monte Carlo

NeurIPS 2001 pp. 1361-1368

/neurips/2001/cemgil2001neurips-tempo/

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 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. The resulting model is suitable for realtime tempo tracking and tran(cid:173) scription and hence useful in a number of music applications such as adaptive automatic accompaniment and score typesetting.

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Cite

Text

Cemgil and Kappen. "Tempo Tracking and Rhythm Quantization by Sequential Monte Carlo." Neural Information Processing Systems, 2001.

Markdown

[Cemgil and Kappen. "Tempo Tracking and Rhythm Quantization by Sequential Monte Carlo." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/cemgil2001neurips-tempo/)

BibTeX

@inproceedings{cemgil2001neurips-tempo,
  title     = {{Tempo Tracking and Rhythm Quantization by Sequential Monte Carlo}},
  author    = {Cemgil, Ali Taylan and Kappen, Bert},
  booktitle = {Neural Information Processing Systems},
  year      = {2001},
  pages     = {1361-1368},
  url       = {https://mlanthology.org/neurips/2001/cemgil2001neurips-tempo/}
}