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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.
|Number of pages||5|
|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||IEEE Workshop on Applications of Signal Processing to Audio and Acoustics - WASPAA 2021|
|Abbreviated title||WASPAA 2021|
|Period||17/10/21 → 20/10/21|
- direction of arrival
- polynomial eigenvalue decomposition
- microphone arrays
- sound source
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