TY - GEN
T1 - Sound source localization using relative harmonic coefficients in modal domain
AU - Hu, Yonggang
AU - Samarasinghe, Prasanga N.
AU - Abhayapala, Thushara D.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - This paper proposes a data-driven source localization approach under a noisy and reverberant environment, using a newly defined feature named relative harmonic coefficients (RHC) in the modal domain. Being independent of the source signal, the RHC is capable of localizing a sound source(s) located at unknown position(s). Two distinctive multi-view Gaussian process (MVGP), (i) multi-frequency views and (ii) multi-mode views, are developed for Gaussian process regression (GPR) to reveal the mapping function from the RHC to the corresponding source location. We evaluate the effectiveness of the algorithm for single source localization while the underlying concepts proposed can be extended to acoustic scenarios where multiple sources are active. Experimental results, using a spherical microphone array, confirm that the proposed algorithm has a faster speed and achieves competitive performance in comparison to the state-of-art algorithm.
AB - This paper proposes a data-driven source localization approach under a noisy and reverberant environment, using a newly defined feature named relative harmonic coefficients (RHC) in the modal domain. Being independent of the source signal, the RHC is capable of localizing a sound source(s) located at unknown position(s). Two distinctive multi-view Gaussian process (MVGP), (i) multi-frequency views and (ii) multi-mode views, are developed for Gaussian process regression (GPR) to reveal the mapping function from the RHC to the corresponding source location. We evaluate the effectiveness of the algorithm for single source localization while the underlying concepts proposed can be extended to acoustic scenarios where multiple sources are active. Experimental results, using a spherical microphone array, confirm that the proposed algorithm has a faster speed and achieves competitive performance in comparison to the state-of-art algorithm.
KW - Gaussian process regression
KW - multi-view Gaussian process
KW - Relative harmonic coefficients
KW - Source localization
UR - http://www.scopus.com/inward/record.url?scp=85078001864&partnerID=8YFLogxK
U2 - 10.1109/WASPAA.2019.8937221
DO - 10.1109/WASPAA.2019.8937221
M3 - Conference contribution
AN - SCOPUS:85078001864
T3 - IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
SP - 348
EP - 352
BT - 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2019
Y2 - 20 October 2019 through 23 October 2019
ER -