Sparse Bayesian Learning for Acoustic Source Localization

Autor:	Ruchi Pandey, Aditya Siripuram, Santosh Nannuru
Rok vydání:	2021
Předmět:	Beamforming Compressed sensing Optimization problem Estimation theory Computer science Location awareness Multiple signal classification Acoustic source localization computer.software_genre Bayesian inference computer Algorithm
Zdroj:	ICASSP
DOI:	10.1109/icassp39728.2021.9413960
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3b3ec3b3785ed5d29d531369521975a9 https://doi.org/10.1109/icassp39728.2021.9413960 Zobrazit plný text záznamu