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A generative model for music transcription

Cemgil, AT; Kappen, HJ; Barber, D; (2006) A generative model for music transcription. IEEE Transactions on Audio, Speech and Language Processing , 14 (2) pp. 679-694. 10.1109/TSA.2005.852985. Green open access

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Abstract

In this paper, we present a graphical model for polyphonic music transcription. Our model, formulated as a dynamical Bayesian network, embodies a transparent and computationally tractable approach to this acoustic analysis problem. An advantage of our approach is that it places emphasis on explicitly modeling the sound generation procedure. It provides a clear framework in which both high level (cognitive) prior information on music structure can be coupled with low level (acoustic physical) information in a principled manner to perform the analysis. The model is a special case of the, generally intractable, switching Kalman filter model. Where possible, we derive, exact polynomial time inference procedures, and otherwise efficient approximations. We argue that our generative model based approach is computationally feasible for many music applications and is readily extensible to more general auditory scene analysis scenarios. © 2006 IEEE.

Type: Article
Title: A generative model for music transcription
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TSA.2005.852985
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/12192
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