Sound field reconstruction using block sparse Bayesian learning equivalent source method
Sound field reconstruction using block sparse Bayesian learning equivalent source method
复制标题
使用块稀疏贝叶斯学习等效源方法进行声场重建
DOI：
10.1121/10.0010103
复制
发表时间：
2022-04-01
影响因子：
2.4
通讯作者：
Zhou, Rong
中科院分区：
文献类型：
--
作者：
Bi, Chuan-Xing;Zhang, Feng-Min;Zhou, Rong
基于压缩传感理论的近场声全息可以在获得合适的稀疏基的前提下，以较少的测量点实现声场的精确重建。然而，对于不同类型的声源，合适的稀疏基是不同的，应该精心构建。提出了一种块稀疏贝叶斯学习(SBL)等效源方法，用于实现不同类型的源辐射声场的重建，包括空间稀疏源、空间扩展源以及空间稀疏源和空间扩展源的混合源，而不需要复杂地构造稀疏基。该方法建立了块稀疏等效源模型，通过对等效源模型施加结构化先验，并利用SBL估计模型的后验信息，提出了块稀疏解，只需调整块大小，即可实现对不同类型声源辐射声场的精确重建。数值仿真和实验结果验证了该方法的有效性和优越性，并通过仿真研究了块大小和稀疏剪枝阈值这两个关键参数的影响。(C)2022美国声学学会。
Nearfield acoustic holography based on the compressed sensing theory can realize the accurate reconstruction of sound fields with fewer measurement points on the premise that an appropriate sparse basis is obtained. However, for different types of sound sources, the appropriate sparse bases are diverse and should be constructed elaborately. In this paper, a block sparse Bayesian learning (SBL) equivalent source method is proposed for realizing the reconstruction of the sound fields radiated by different types of sources, including the spatially sparse sources, the spatially extended sources, and the mixed ones of the above two, without the elaborate construction of the sparse basis. The proposed method constructs a block sparse equivalent source model and promotes a block sparse solution by imposing a structured prior on the equivalent source model and estimating the posterior of the model by using the SBL, which can achieve the accurate reconstruction of the radiated sound fields of different types of sources simply by adjusting the block size. Numerical simulation and experimental results demonstrate the validity and superiority of the proposed method, and the effects of two key parameters, the block size, and sparsity pruning threshold value are investigated through simulations.(c) 2022 Acoustical Society of America.