Compressive Spherical Beamforming Based on Sparse Bayesian Learning for Sound Source Identification

doi:10.3901/JME.2023.02.001

Abstract

Abstract: Compressive spherical beamforming(CSB) based on solid spherical microphone array enjoys the advantages of panoramic sound imaging, suitable for medium and long distance measurement and easy to arrange, so it has broad application prospects in the field of noise source identification such as automobiles and airplanes. The recently proposed CSB based on Maximum type-II likelihood estimation(MLE-II) under sparse Bayesian learning(SBL) framework can achieve good low-frequency sound source identification performance, but it needs to estimate the sound source sparsity, and the anti-noise interference ability and computational efficiency also need to be improved. To solve the above problems, MAP-EM-based CSB(MAP-EM-CSB) without sound source sparsity estimation is proposed, which transforms the mathematical model solving problem of CSB into the maximum a posteriori(MAP) estimation problem of the source intensity distribution under SBL framework, and then uses the expectation maximization(EM) algorithm to solve it. Further, the sound pressure complex matrix input of MAP-EM-CSB is converted into the average sound pressure cross-spectrum matrix input of multiple snapshots, and then the enhanced MAP-EM-CSB(EMAP-EM-CSB) is established based on the diagonal reconstruction and noise reduction of the cross-spectrum matrix. Both simulations and experiment show that the proposed MAP-EM-CSB and EMAP-EM-CSB have high spatial resolution and computational efficiency. Due to its strong anti-noise interference capability, EMAP-EM-CSB has better sound source identification performance, especially in low frequency and low signal-to-noise ratio environments. Finally, the effects of the number of iterations and snapshots on the performance of MAP-EM-CSB and EMAP-EM-CSB are analyzed, and the recommended values are obtained.

Key words: sound source identification, spherical microphone array, compressive spherical beamforming, sparse Bayesian learning

CLC Number:

TB52

CHU Zhigang, ZHAO Yang, YANG Liang, ZHANG Jinyuan, YANG Yang. Compressive Spherical Beamforming Based on Sparse Bayesian Learning for Sound Source Identification[J]. Journal of Mechanical Engineering, 2023, 59(2): 1-13.

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