A polynomial eigenvalue decomposition MUSIC approach for broadband sound source localization
A polynomial eigenvalue decomposition MUSIC approach for broadband sound source localization
Direction of arrival (DoA) estimation for sound source localization is increasingly prevalent in modern devices. In this paper, we explore a polynomial extension to the multiple signal classification (MUSIC) algorithm, spatio-spectral polynomial (SSP)-MUSIC, and evaluate its performance when using speech sound sources. In addition, we also propose three essential enhancements for SSP-MUSIC to work with noisy reverberant audio data. This paper includes an analysis of SSP-MUSIC using speech signals in a simulated room for different noise and reverberation conditions and the first task of the LOCATA challenge. We show that SSP-MUSIC is more robust to noise and reverberation compared to independent frequency bin (IFB) approaches and improvements can be seen for single sound source localization at signal-to-noise ratios (SNRs) below 5 dB and reverberation times (T60s) larger than 0.7 s.
Hogg, Aidan
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Neo, Vincent W.
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Weiss, Stephan
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Evers, Christine
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Naylor, Patrick
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Hogg, Aidan
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Neo, Vincent W.
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Weiss, Stephan
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Evers, Christine
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Naylor, Patrick
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Hogg, Aidan, Neo, Vincent W., Weiss, Stephan, Evers, Christine and Naylor, Patrick
(2021)
A polynomial eigenvalue decomposition MUSIC approach for broadband sound source localization.
In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).
IEEE.
5 pp
.
(In Press)
Record type:
Conference or Workshop Item
(Paper)
Abstract
Direction of arrival (DoA) estimation for sound source localization is increasingly prevalent in modern devices. In this paper, we explore a polynomial extension to the multiple signal classification (MUSIC) algorithm, spatio-spectral polynomial (SSP)-MUSIC, and evaluate its performance when using speech sound sources. In addition, we also propose three essential enhancements for SSP-MUSIC to work with noisy reverberant audio data. This paper includes an analysis of SSP-MUSIC using speech signals in a simulated room for different noise and reverberation conditions and the first task of the LOCATA challenge. We show that SSP-MUSIC is more robust to noise and reverberation compared to independent frequency bin (IFB) approaches and improvements can be seen for single sound source localization at signal-to-noise ratios (SNRs) below 5 dB and reverberation times (T60s) larger than 0.7 s.
Text
m20821-hogg
- Author's Original
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Accepted/In Press date: 14 July 2021
Identifiers
Local EPrints ID: 450812
URI: http://eprints.soton.ac.uk/id/eprint/450812
PURE UUID: 3986e06a-d5eb-400a-8843-f904da2fd24f
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Date deposited: 12 Aug 2021 16:31
Last modified: 17 Mar 2024 04:01
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Contributors
Author:
Aidan Hogg
Author:
Vincent W. Neo
Author:
Stephan Weiss
Author:
Christine Evers
Author:
Patrick Naylor
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