In this section
@inproceedings{Hogg:2021:10.1109/WASPAA52581.2021.9632789,
author = {Hogg, A and Neo, V and Weiss, S and Evers, C and Naylor, P},
doi = {10.1109/WASPAA52581.2021.9632789},
pages = {326--330},
publisher = {IEEE},
title = {A polynomial eigenvalue decomposition MUSIC approach for broadband sound source localization},
url = {http://dx.doi.org/10.1109/WASPAA52581.2021.9632789},
year = {2021}
}
TY - CPAPER
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 (SSP)-MUSIC, and evaluate its performance when using speech sound sources. In addition, we also propose three essential enhancements for SSP-MUSIC to work with noisy reverberant audio data. This paper includes an analysis of SSP-MUSIC using speech signals in a simulated room for different noise and reverberation conditions and the first task of the LOCATA challenge. We show 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 ratios (SNRs) below 5 dB and reverberation times (T60s) larger than 0.7 s.
AU - Hogg,A
AU - Neo,V
AU - Weiss,S
AU - Evers,C
AU - Naylor,P
DO - 10.1109/WASPAA52581.2021.9632789
EP - 330
PB - IEEE
PY - 2021///
SP - 326
TI - A polynomial eigenvalue decomposition MUSIC approach for broadband sound source localization
UR - http://dx.doi.org/10.1109/WASPAA52581.2021.9632789
UR - https://ieeexplore.ieee.org/document/9632789
UR - http://hdl.handle.net/10044/1/90861
ER -
For more information about the group, please contact:
Dr Dan Goodman
+44 (0)20 7594 6264
d.goodman@imperial.ac.uk