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Publication year
2006Source
IEEE Transactions on Speech and Audio Processing, 14, 2, (2006), pp. 679-694ISSN
Publication type
Article / Letter to editor

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Organization
Cognitive Neuroscience
Biophysics
Former Organization
Medical Physics and Biophysics
Journal title
IEEE Transactions on Speech and Audio Processing
Volume
vol. 14
Issue
iss. 2
Page start
p. 679
Page end
p. 694
Subject
BiophysicsAbstract
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 modelling 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 Filters. 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.
This item appears in the following Collection(s)
- Academic publications [204996]
- Electronic publications [103294]
- Faculty of Medical Sciences [81051]
- Faculty of Science [32345]
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