A polynomial eigenvalue decomposition MUSIC approach for broadband sound source localization

Hogg, Aidan O. T. and Neo, Vincent W. and Weiss, Stephan and Evers, Christine and Naylor, Patrick A. (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 2021, 2021-10-17 - 2021-10-20.

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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.

ORCID iDs

Hogg, Aidan O. T., Neo, Vincent W., Weiss, Stephan ORCID logoORCID: https://orcid.org/0000-0002-3486-7206, Evers, Christine and Naylor, Patrick A.;