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

Research output: Contribution to conferencePaperpeer-review

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 languageEnglish
Pages1-5
Number of pages5
Publication statusAccepted/In press - 14 Jul 2021
EventIEEE Workshop on Applications of Signal Processing to Audio and Acoustics - WASPAA 2021 - New Paltz, United States
Duration: 17 Oct 202120 Oct 2021

Workshop

WorkshopIEEE Workshop on Applications of Signal Processing to Audio and Acoustics - WASPAA 2021
Abbreviated titleWASPAA 2021
Country/TerritoryUnited States
CityNew Paltz
Period17/10/2120/10/21

Keywords

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

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