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Source tracking using moving microphone arrays for robot audition

Source tracking using moving microphone arrays for robot audition
Source tracking using moving microphone arrays for robot audition

Intuitive spoken dialogues are a prerequisite for human-robot interaction. In many practical situations, robots must be able to identify and focus on sources of interest in the presence of interfering speakers. Techniques such as spatial filtering and blind source separation are therefore often used, but rely on accurate knowledge of the source location. In practice, sound emitted in enclosed environments is subject to reverberation and noise. Hence, sound source localization must be robust to both diffuse noise due to late reverberation, as well as spurious detections due to early reflections. For improved robustness against reverberation, this paper proposes a novel approach for sound source tracking that constructively exploits the spatial diversity of a microphone array installed in a moving robot. In previous work, we developed speaker localization approaches using expectation-maximization (EM) approaches and using Bayesian approaches. In this paper we propose to combine the EM and Bayesian approach in one framework for improved robustness against reverberation and noise.

Acoustic Signal Processing, Bayesian estimation, Expectation-Maximization, Particle filter, Sound Source Tracking
6145-6149
Institute of Electrical and Electronics Engineers Inc.
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Dorfan, Yuval
195e7116-b434-49b7-a752-5627e0a4f877
Gannot, Sharon
c66353d3-e91c-4e9a-8b01-920bb617cd70
Naylor, Patrick A.
13079486-664a-414c-a1a2-01a30bf0997b
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Dorfan, Yuval
195e7116-b434-49b7-a752-5627e0a4f877
Gannot, Sharon
c66353d3-e91c-4e9a-8b01-920bb617cd70
Naylor, Patrick A.
13079486-664a-414c-a1a2-01a30bf0997b

Evers, Christine, Dorfan, Yuval, Gannot, Sharon and Naylor, Patrick A. (2017) Source tracking using moving microphone arrays for robot audition. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. pp. 6145-6149 . (doi:10.1109/ICASSP.2017.7953337).

Record type: Conference or Workshop Item (Paper)

Abstract

Intuitive spoken dialogues are a prerequisite for human-robot interaction. In many practical situations, robots must be able to identify and focus on sources of interest in the presence of interfering speakers. Techniques such as spatial filtering and blind source separation are therefore often used, but rely on accurate knowledge of the source location. In practice, sound emitted in enclosed environments is subject to reverberation and noise. Hence, sound source localization must be robust to both diffuse noise due to late reverberation, as well as spurious detections due to early reflections. For improved robustness against reverberation, this paper proposes a novel approach for sound source tracking that constructively exploits the spatial diversity of a microphone array installed in a moving robot. In previous work, we developed speaker localization approaches using expectation-maximization (EM) approaches and using Bayesian approaches. In this paper we propose to combine the EM and Bayesian approach in one framework for improved robustness against reverberation and noise.

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More information

Published date: 16 June 2017
Venue - Dates: 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017, , New Orleans, United States, 2017-03-05 - 2017-03-09
Keywords: Acoustic Signal Processing, Bayesian estimation, Expectation-Maximization, Particle filter, Sound Source Tracking

Identifiers

Local EPrints ID: 445100
URI: http://eprints.soton.ac.uk/id/eprint/445100
PURE UUID: 769937ed-7066-4ca8-81a2-7440f0d4e861
ORCID for Christine Evers: ORCID iD orcid.org/0000-0003-0757-5504

Catalogue record

Date deposited: 19 Nov 2020 17:32
Last modified: 19 Nov 2020 17:32

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Contributors

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

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