Source Localization by Multidimensional Steered Response Power Mapping with Sparse Bayesian Learning

TL;DR

This paper introduces a multidimensional sparse Bayesian SRP method that enhances source localization accuracy, especially for closely spaced sources in reverberant environments, by leveraging sparsity and preserving time-frequency information.

Contribution

It presents a novel multidimensional SRP framework with sparse Bayesian learning that improves localization resolution without prior source number knowledge.

Findings

Outperforms conventional SRP in reverberant conditions.

Achieves higher resolution in localizing closely spaced sources.

Demonstrates effectiveness with real microphone array data.

Abstract

We propose an advance Steered Response Power (SRP) method for localizing multiple sources. While conventional SRP performs well in adverse conditions, it remains to struggle in scenarios with closely neighboring sources, resulting in ambiguous SRP maps. We address this issue by applying sparsity optimization in SRP to obtain high-resolution maps. Our approach represents SRP maps as multidimensional matrices to preserve time-frequency information and further improve performance in unfavorable conditions. We use multi-dictionary Sparse Bayesian Learning to localize sources without needing prior knowledge of their quantity. We validate our method through practical experiments with a 16-channel planar microphone array and compare against three other SRP and sparsity-based methods. Our multidimensional SRP approach outperforms conventional SRP and the current state-of-the-art sparse SRP methods for localizing closely spaced sources in a reverberant room.