Sound source identification using the boundary element method in a sparse framework

• Published in: Journal of vibration and control Vol. 30; no. 15-16; pp. 3449 - 3461
• Main Authors: Xiao, Youhong, Zhang, Zixin, Fei, Jingzhou, Lu, Huabing, Zhang, Chenyu
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-08-2024; SAGE PUBLICATIONS, INC
• Subjects: Algorithms; Boundary element method; Holography; Machine learning; Reconstruction; Sampling; Signal to noise ratio; Singular value decomposition; Sound pressure; Sound sources; Steepest descent method; Surface velocity; Transfer matrices; sound source identification; near-field acoustic holography; boundary element method; sparse Bayesian learning
• ISSN: 1077-5463; 1741-2986
• DOI: 10.1177/10775463231194705

Traditional near-field acoustic holography (NAH) based on the boundary element method (BEM) exhibits good performance for sound source identification. However, good reconstruction results can only be guaranteed if a sufficient number of sampling points are used. In this paper, the unitary matrix is used as the sparse basis matrix of source surface velocity and is obtained from the transfer matrix between the source surface sound pressure and vibration velocity by singular value decomposition (SVD). Based on this sparse basis, a sparse Bayesian learning (SBL) algorithm is introduced into BEM-based NAH to address the case of few sampling points. The use of a standard SBL algorithm in BEM-based NAH leads to hyperparameters that are overly sparse due to the large number of iterations, which results in large reconstruction errors. To solve this problem, a modified SBL algorithm is proposed. Good reconstruction results are maintained by selecting appropriate hyperparameters and revising the results using the steepest descent method. A simulated cuboid shell is used to explore the influence of the (i) number of sampling points and (ii) signal-to-noise ratio (SNR) on the reconstruction. Numerical results show that the proposed method can achieve effective reconstruction when the number of sampling points is approximately one-tenth of the number of discrete points on the source surface. The validity of the proposed method is verified by a comparison with experimental results.