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Speaker localization with moving microphone arrays

Speaker localization with moving microphone arrays
Speaker localization with moving microphone arrays

Speaker localization algorithms often assume static location for all sensors. This assumption simplifies the models used, since all acoustic transfer functions are linear time invariant. In many applications this assumption is not valid. In this paper we address the localization challenge with moving microphone arrays. We propose two algorithms to find the speaker position. The first approach is a batch algorithm based on the maximum likelihood criterion, optimized via expectationmaximization iterations. The second approach is a particle filter for sequential Bayesian estimation. The performance of both approaches is evaluated and compared for simulated reverberant audio data from a microphone array with two sensors.

2219-5491
1003-1007
European Signal Processing Conference, EUSIPCO
Dorfan, Yuval
195e7116-b434-49b7-a752-5627e0a4f877
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Gannot, Sharon
c66353d3-e91c-4e9a-8b01-920bb617cd70
Naylor, Patrick A.
13079486-664a-414c-a1a2-01a30bf0997b
Dorfan, Yuval
195e7116-b434-49b7-a752-5627e0a4f877
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Gannot, Sharon
c66353d3-e91c-4e9a-8b01-920bb617cd70
Naylor, Patrick A.
13079486-664a-414c-a1a2-01a30bf0997b

Dorfan, Yuval, Evers, Christine, Gannot, Sharon and Naylor, Patrick A. (2016) Speaker localization with moving microphone arrays. In 2016 24th European Signal Processing Conference, EUSIPCO 2016. vol. 2016-November, European Signal Processing Conference, EUSIPCO. pp. 1003-1007 . (doi:10.1109/EUSIPCO.2016.7760399).

Record type: Conference or Workshop Item (Paper)

Abstract

Speaker localization algorithms often assume static location for all sensors. This assumption simplifies the models used, since all acoustic transfer functions are linear time invariant. In many applications this assumption is not valid. In this paper we address the localization challenge with moving microphone arrays. We propose two algorithms to find the speaker position. The first approach is a batch algorithm based on the maximum likelihood criterion, optimized via expectationmaximization iterations. The second approach is a particle filter for sequential Bayesian estimation. The performance of both approaches is evaluated and compared for simulated reverberant audio data from a microphone array with two sensors.

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More information

Published date: 28 November 2016
Venue - Dates: 24th European Signal Processing Conference, EUSIPCO 2016, , Budapest, Hungary, 2016-08-27 - 2016-09-01

Identifiers

Local EPrints ID: 446093
URI: http://eprints.soton.ac.uk/id/eprint/446093
ISSN: 2219-5491
PURE UUID: 0b9a019a-6721-4a47-98b2-c7cd388c7359
ORCID for Christine Evers: ORCID iD orcid.org/0000-0003-0757-5504

Catalogue record

Date deposited: 20 Jan 2021 17:32
Last modified: 18 Feb 2021 17:41

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Contributors

Author: Yuval Dorfan
Author: Christine Evers ORCID iD
Author: Sharon Gannot
Author: Patrick A. Naylor

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