Achieving the sparse acoustical holography via the sparse bayesian learning.

• The SBL algorithm is used for sound source localization and quantification. • The SBL and IBF algorithms are compared in terms of effectiveness and robustness. • Advantages of SBL at low SNRs, low frequency are validated through experiments. The localization accuracy and acoustic quantification are the leading indicators of acoustic localization. It is difficult to reconstruct the acoustic field completely as the number of sources is larger than the number of microphones. To solve this problem, the sparse acoustic holography under the Bayesian framework is applied to acquire the phase and amplitude distribution of the acoustic field to achieve acoustic source localization. In this paper, a Sparse Bayesian Learning (SBL) algorithm is improved, which can not only perform acoustic localization quickly and accurately, but also quantize the sound source to achieve sparse acoustic holography. To verify the efficiency and robustness of the improved method, simulations and experiments with different sound sources and noise disturbances are performed in this paper to verify the superior performance of the SBL algorithm at low frequencies and low signal-to-noise ratios (SNR). [ABSTRACT FROM AUTHOR]