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.
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Document Type: Research Article
Publication date: May 1, 2018
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- Acta Acustica united with Acustica, published together with the European Acoustics Association (EAA), is an international, peer-reviewed journal on acoustics. It publishes original articles on all subjects in the field of acoustics, such as general linear acoustics, nonlinear acoustics, macrosonics, flow acoustics, atmospheric sound, underwater sound, ultrasonics, physical acoustics, structural acoustics, noise control, active control, environmental noise, building acoustics, room acoustics, acoustic materials, acoustic signal processing, computational and numerical acoustics, hearing, audiology and psychoacoustics, speech, musical acoustics, electroacoustics, auditory quality of systems. It reports on original scientific research in acoustics and on engineering applications. The journal considers scientific papers, technical and applied papers, book reviews, short communications, doctoral thesis abstracts, etc. In irregular intervals also special issues and review articles are published.
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