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Sound source visualization has been widely used in many scenarios such as aerospace, industrial production, and urban management. Sound source localization technology plays an essential role in the realization of sound source visualization. The imaging results and calculation cost are the primary considerations in the problem of sound source localization. This article proposes a subspace iteration integrated variational Bayesian (SVB) method to realize adaptive imaging of different sound sources. First, the proposed variational Bayesian (VB) method is based on total variation (TV) prior to balance the sparsity and smoothness of the imaging results. Second, the subspace optimization method in the probability measure space is integrated into the proposed SVB method to solve the involved ill-posed inverse problem. The proposed SVB method can significantly improve the calculation speed, especially for large-scale inverse problems. Finally, the speed and robustness of the proposed SVB method can be demonstrated according to the extensive results of simulation and experimental validation. [ABSTRACT FROM AUTHOR]

The acoustic imaging results in the diffuse field are seriously affected by reverberation. The acoustic source localization algorithms based on the free-field assumption will produce many artifacts in the reverberant environment. To achieve accurate acoustic source localization in a reverberant environment, a sound field morphological component analysis (SF-MCA) model and an enhanced alternating direction method of multipliers (ADMM) algorithm are proposed in this article. First, the solution of the inhomogeneous Helmholtz equation is analyzed to characterize the acoustic components. Second, Green’s function and plane-wave basis function serve as dictionaries for sparse representation of the acoustic signal in the frequency domain, and the corresponding decomposition coefficients are obtained by the enhanced ADMM algorithm. Finally, the accurate acoustic imaging results are realized in the reverberant chamber space with strong reverberation ($T_{20}=174.30\,\,\text {ms}$). Experiments demonstrate the validation of the proposed SF-MCA method. The acoustic imaging obtained by the proposed SF-MAC dereverberation model is far superior to the acoustic imaging that treats the reverberation as the general Gaussian noise. [ABSTRACT FROM AUTHOR]