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

9 Citations (Scopus)

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-WASPAA2021-A-polynomial-eigenvalue-decomposition-music-approach-broadband-sound-source-localizationAccepted author manuscript, 443 KB

Signal Processing in the Information Age (UDRC III)
Weiss, S. & Stankovic, V.
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.",

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doi = "10.1109/WASPAA52581.2021.9632789",

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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.

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

Access to Document

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Projects

Signal Processing in the Information Age (UDRC III)

Cite this