A polynomial eigenvalue decomposition MUSIC approach for broadband sound source localization

Aidan O. T. Hogg; Vincent W. Neo; Stephan Weiss; Christine Evers; Patrick A. Naylor

doi:10.1109/WASPAA52581.2021.9632789

A polynomial eigenvalue decomposition MUSIC approach for broadband sound source localization

Aidan O. T. Hogg, Vincent W. Neo, Stephan Weiss, Christine Evers, Patrick A. Naylor

Electronic And Electrical Engineering

Research output: Contribution to conference › Paper › peer-review

19 Citations (Scopus)

20 Downloads (Pure)

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 MUSIC (SSP-MUSIC), and evaluate its performance when using speech sound sources. The paper includes an analysis of SSP-MUSIC using speech signals in a simulated room for different conditions in terms of diffuse noise and reverberation. SSP-MUSIC is also evaluated on the first task of the LOCATA challenge. This paper shows 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 ratio (SNR) values lower than 5 dB and reverberation time (T60) values larger than 0.7 s.

Original language	English
Pages	1-5
Number of pages	5
DOIs	https://doi.org/10.1109/WASPAA52581.2021.9632789
Publication status	Published - 20 Oct 2021
Event	IEEE Workshop on Applications of Signal Processing to Audio and Acoustics - WASPAA 2021 - New Paltz, United States Duration: 17 Oct 2021 → 20 Oct 2021

Workshop

Workshop	IEEE Workshop on Applications of Signal Processing to Audio and Acoustics - WASPAA 2021
Abbreviated title	WASPAA 2021
Country/Territory	United States
City	New Paltz
Period	17/10/21 → 20/10/21

Keywords

direction of arrival
polynomial eigenvalue decomposition
localization
microphone arrays
music
sound source

Access to Document

10.1109/WASPAA52581.2021.9632789

Hogg-etal-WASPAA-2021-A-polynomial-eigenvalue-decomposition-music-approach-broadbandAccepted author manuscript, 740 KBLicence: Strath_1

Signal Processing in the Information Age (UDRC III)
Weiss, S. (Principal Investigator) & Stankovic, V. (Co-investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/07/18 → 31/03/24
Project: Research

Cite this

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title = "A polynomial eigenvalue decomposition MUSIC approach for broadband sound source localization",

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 MUSIC (SSP-MUSIC), and evaluate its performance when using speech sound sources. The paper includes an analysis of SSP-MUSIC using speech signals in a simulated room for different conditions in terms of diffuse noise and reverberation. SSP-MUSIC is also evaluated on the first task of the LOCATA challenge. This paper shows 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 ratio (SNR) values lower than 5 dB and reverberation time (T60) values larger than 0.7 s.",

keywords = "direction of arrival, polynomial eigenvalue decomposition, localization, microphone arrays, music, sound source",

author = "Hogg, {Aidan O. T.} and Neo, {Vincent W.} and Stephan Weiss and Christine Evers and Naylor, {Patrick A.}",

note = "{\textcopyright} 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; IEEE Workshop on Applications of Signal Processing to Audio and Acoustics - WASPAA 2021, WASPAA 2021 ; Conference date: 17-10-2021 Through 20-10-2021",

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language = "English",

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A polynomial eigenvalue decomposition MUSIC approach for broadband sound source localization. / Hogg, Aidan O. T.; Neo, Vincent W.; Weiss, Stephan et al.
2021. 1-5 Paper presented at IEEE Workshop on Applications of Signal Processing to Audio and Acoustics - WASPAA 2021, New Paltz, New York, United States.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - A polynomial eigenvalue decomposition MUSIC approach for broadband sound source localization

AU - Hogg, Aidan O. T.

AU - Neo, Vincent W.

AU - Weiss, Stephan

AU - Evers, Christine

AU - Naylor, Patrick A.

N1 - © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2021/10/20

Y1 - 2021/10/20

N2 - 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 MUSIC (SSP-MUSIC), and evaluate its performance when using speech sound sources. The paper includes an analysis of SSP-MUSIC using speech signals in a simulated room for different conditions in terms of diffuse noise and reverberation. SSP-MUSIC is also evaluated on the first task of the LOCATA challenge. This paper shows 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 ratio (SNR) values lower than 5 dB and reverberation time (T60) values larger than 0.7 s.

AB - 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 MUSIC (SSP-MUSIC), and evaluate its performance when using speech sound sources. The paper includes an analysis of SSP-MUSIC using speech signals in a simulated room for different conditions in terms of diffuse noise and reverberation. SSP-MUSIC is also evaluated on the first task of the LOCATA challenge. This paper shows 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 ratio (SNR) values lower than 5 dB and reverberation time (T60) values larger than 0.7 s.

KW - direction of arrival

KW - polynomial eigenvalue decomposition

KW - localization

KW - microphone arrays

KW - music

KW - sound source

U2 - 10.1109/WASPAA52581.2021.9632789

DO - 10.1109/WASPAA52581.2021.9632789

M3 - Paper

SP - 1

EP - 5

T2 - IEEE Workshop on Applications of Signal Processing to Audio and Acoustics - WASPAA 2021

Y2 - 17 October 2021 through 20 October 2021

ER -

A polynomial eigenvalue decomposition MUSIC approach for broadband sound source localization

Abstract

Workshop

Keywords

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Projects

Signal Processing in the Information Age (UDRC III)

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