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A polynomial eigenvalue decomposition MUSIC approach for broadband sound source localization

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.
IEEE
Hogg, Aidan
e2c97ca1-9ec2-4da1-9fd3-5feea6142756
Neo, Vincent W.
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Weiss, Stephan
a89960cd-f869-4728-8f8e-c0b60f04f911
Evers, Christine
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Naylor, Patrick
8c20a1a0-4507-4a0f-8324-f3075354dc52
Hogg, Aidan
e2c97ca1-9ec2-4da1-9fd3-5feea6142756
Neo, Vincent W.
7ec5cc5f-8248-40ec-8864-b31335d4ddf2
Weiss, Stephan
a89960cd-f869-4728-8f8e-c0b60f04f911
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Naylor, Patrick
8c20a1a0-4507-4a0f-8324-f3075354dc52

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.

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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
ORCID for Christine Evers: ORCID iD orcid.org/0000-0003-0757-5504

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Date deposited: 12 Aug 2021 16:31
Last modified: 13 Aug 2021 02:02

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Contributors

Author: Aidan Hogg
Author: Vincent W. Neo
Author: Stephan Weiss
Author: Christine Evers ORCID iD
Author: Patrick Naylor

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