Indoor Sound Source Localization via Inverse Element-Free Simulation Based on Joint Sparse Recovery

Abstract

Indoor sound source localization is a key technique in many engineering applications, and an inverse element-free method based on joint sparse recovery in a Bayesian framework is proposed for reverberant environments. In this method, a discrete wave model is constructed to represent the relationships between the sampled sound pressure and the source intensity distribution, and localization in the reverberant environment is realized via inversion from the wave model. By constructing a compact supporting domain, the source intensity can be sparsely represented in subdomains, and the sparse Bayesian framework is used to recover the source intensity. In particular, joint sparse recovery in the frequency domain is exploited to improve the recovery performance. Numerical and experimental verifications show that, compared with another state-of-the-art method, the proposed method achieves high source-localization accuracy and low sidelobes with low computational complexity in highly reverberant environments.