Acoustic source localization in strong reverberant environment by parametric Bayesian dictionary learning

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A parametric Bayesian dictionary learning approach is proposed for source localization problem under unknown strong reverberate environment. In particular, a parametric dictionary is constructed by discretizing the inner space of the enclosure, which formulates the reverberations as part of the signal model. Thereafter, the problem of localization can be considered as the sparse signal recovery and dictionary learning problem.In contrast to the conventional sparsity based source localization techniques, the dictionary is constructed in a parametric manner and will be updated during estimation in the proposed approach. Since unknown reverberations can be automatically estimated from the measurements by the proposed method, they can be appropriately exploited to enhance the accuracy of source localization.Since the proposed algorithm is constructed in a Bayesian framework, the proposed algorithm can able to obtain the capability of distinguishing highly correlated atoms in a dictionary, flexibility of modeling and capability of providing statistical information.Our simulated results have shown that the proposed algorithm can achieve high source localization accuracy, low sidelobes and high robustness for multiple sources with low computational complexity in strong reverberant environments compared with other state-of-the-art methods. Sparse representation techniques have become increasingly promising for localizing the sound source in reverberant environment, where the multipath channel effects can be accurately characterized by the image model. In this paper, a dictionary is constructed by discretizing the inner space of the enclosure, which is parameterized by the unknown energy reflective ratio. More specifically, each atom of the dictionary can characterize a specific source-to-microphone multipath channel. Subsequently, source localization can be reformulated as a joint sparse signal recovery and parametric dictionary learning problem. In particular, a sparse Bayesian framework is utilized for modeling, where its solution can be obtained by variational Bayesian expectation maximization technique. Moreover, the joint sparsity in frequency domain is exploited to improve the dictionary learning performances. A remarkably advantage of this approach is that no laborious parameter tuning procedure is required and statistical information can be provided. Numerical simulation results have shown that the proposed algorithm achieves high source localization accuracy, low sidelobes and high robustness for multiple sources with low computational complexity in strong reverberant environments, compared with other state-of-the-art methods.