| Paper ID | SAM-12.4 |
| Paper Title |
SPARSE BAYESIAN LEARNING FOR ACOUSTIC SOURCE LOCALIZATION |
| Authors |
Ruchi Pandey, Santosh Nannuru, IIIT Hyderabad, India; Aditya Siripuram, IIT Hyderabad, India |
| Session | SAM-12: Tracking and Localization |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Sensor Array and Multichannel Signal Processing: [SAM-DOAE] Direction of arrival estimation and source localization |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
The localization of acoustic sources is a parameter estimation problem where the parameters of interest are the direction of arrivals (DOAs). The DOA estimation problem can be formulated as a sparse parameter estimation problem and solved using compressive sensing (CS) methods. In this paper, the CS method of sparse Bayesian learning (SBL) is used to find the DOAs. We specifically use multi-frequency SBL leading to a non-convex optimization problem, which is solved using fixed-point iterations. We evaluate SBL along with traditional DOA estimation methods of conventional beamforming (CBF) and multiple signal classification (MUSIC) on various source localization tasks from the open access LOCATA dataset. The comparative study shows that SBL significantly outperforms CBF and MUSIC on all the considered tasks. |
