Sound source localization in reverberant environments based on structural sparse Bayesian learning

The sound source localization in reverberant rooms is reformulated as a joint-sparsity support recovery problem in frequency domain under sparse Bayesian learning framework, where the reverberant effect is characterized using the image model. The joint sparsity in different frequencies is imposed by hierarchical probabilistic modeling with its hidden variables estimated by variational Bayesian inference. Numerical simulation results indicate that the proposed method achieves accurate sound source localization under low signal to noise ratio. The algorithm is evaluated by real data experiments using signals recorded in an anechoic chamber with one reflective plate and a rectangular room with strong reverberation. Both the numerical simulations and the real data experiments indicate that the proposed method can be applied in reverberant environments.