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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.
Original language | English |
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Pages | 1-5 |
Number of pages | 5 |
DOIs | |
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 |
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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
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Signal Processing in the Information Age (UDRC III)
EPSRC (Engineering and Physical Sciences Research Council)
1/07/18 → 31/03/24
Project: Research