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dc.contributor.authorBenetos, E
dc.contributor.authorLafay, G
dc.contributor.authorLagrange, M
dc.contributor.authorPlumbley, MD
dc.date.accessioned2017-02-13T15:08:55Z
dc.date.issued2017-02-13
dc.date.submitted2017-02-08T10:04:53.161Z
dc.identifier.citationBenetos, E, Lafay, G, Lagrange, M and Plumbley, MD (2017) Polyphonic Sound Event Tracking using Linear Dynamical Systems IEEE/ACM Transactions on Audio, Speech, and Language Processing.
dc.identifier.issn2329-9304
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/19368
dc.description.abstractIn this paper, a system for polyphonic sound event detection and tracking is proposed, based on spectrogram factorisation techniques and state space models. The system extends probabilistic latent component analysis (PLCA) and is modelled around a 4-dimensional spectral template dictionary of frequency, sound event class, exemplar index, and sound state. In order to jointly track multiple overlapping sound events over time, the integration of linear dynamical systems (LDS) within the PLCA inference is proposed. The system assumes that the PLCA sound event activation is the (noisy) observation in an LDS, with the latent states corresponding to the true event activations. LDS training is achieved using fully observed data, making use of ground truth-informed event activations produced by the PLCA-based model. Several LDS variants are evaluated, using polyphonic datasets of office sounds generated from an acoustic scene simulator, as well as real and synthesized monophonic datasets for comparative purposes. Results show that the integration of LDS tracking within PLCA leads to an improvement of +8.5-10.5% in terms of frame-based F-measure as compared to the use of the PLCA model alone. In addition, the proposed system outperforms several state-of-the-art methods for the task of polyphonic sound event detection.
dc.format.extent? - ? (12)
dc.publisherIEEE
dc.relation.ispartofIEEE/ACM Transactions on Audio, Speech and Language Processing
dc.titlePolyphonic Sound Event Tracking using Linear Dynamical Systems
dc.typeJournal Article
dc.rights.holder© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
pubs.organisational-group/Queen Mary University of London
pubs.organisational-group/Queen Mary University of London/Faculty of Science & Engineering
pubs.organisational-group/Queen Mary University of London/Faculty of Science & Engineering/Electronic Engineering and Computer Science - Staff
pubs.publication-statusAccepted


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