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.
1003-1007
European Signal Processing Conference
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
28 November 2016
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.
.
(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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Published date: 28 November 2016
Venue - Dates:
24th European Signal Processing Conference, EUSIPCO 2016, , Budapest, Hungary, 2016-08-28 - 2016-09-02
Identifiers
Local EPrints ID: 446093
URI: http://eprints.soton.ac.uk/id/eprint/446093
ISSN: 2219-5491
PURE UUID: 0b9a019a-6721-4a47-98b2-c7cd388c7359
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Date deposited: 20 Jan 2021 17:32
Last modified: 17 Mar 2024 04:01
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Contributors
Author:
Yuval Dorfan
Author:
Christine Evers
Author:
Sharon Gannot
Author:
Patrick A. Naylor
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