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research-article

Fast sparse reconstruction of sound field via Bayesian compressive sensing

[+] Author and Article Information
Ding-Yu Hu

333 Longteng Road, Shanghai 201620, People's Republic of China Shanghai, Shanghai 201620 China dyhu1987@163.com

Xin-Yue Liu

333 Longteng Road, Shanghai 201620, People's Republic of China Shanghai, Shanghai 201620 China 673338069@qq.com

Yue Xiao

289 Tianxiang Avenue, Nanchang 330099, People's Republic of China Nanchang, Jiangxi 330099 China popxy90@163.com

Yu Fang

333 Longteng Road, Shanghai 201620, People's Republic of China Shanghai, Shanghai 201620 China fangyu@sues.edu.cn

1Corresponding author.

Contributed by the Noise Control and Acoustics Division of ASME for publication in the Journal of Vibration and Acoustics. Manuscript received April 8, 2018; final manuscript received March 13, 2019; published online xx xx, xxxx. Assoc. Editor: I. Y. (Steve) Shen.

ASME doi:10.1115/1.4043239 History: Received April 08, 2018; Accepted March 13, 2019

Abstract

To overcome the contradiction between the resolution and the measurement cost, various algorithms for reconstructing the sound field with sparse measurement have been developed. However, limited attention is paid to the computation efficiency. In this study, a fast sparse reconstruction method is proposed based on the Bayesian compressive sensing. First, the reconstruction problem is modeled by a sparse decomposition of the sound field via singular value decomposition. Then, the Bayesian compressive sensing is adapted to reconstruct the sound field with sparse measurement of sound pressure. Numerical results demonstrate that the proposed method is applicable to either the spatially sparse distributed sound sources or the spatially extended sound sources. And comparisons with other two sparse reconstruction methods show that the proposed has the advantages in terms of reconstruction accuracy and computational efficiency. In addition, In addition, as it is developed in the framework of multi-task compressive sensing, the method can use multiple snapshots to perform reconstruction, which greatly enhances the robustness to noise. The validity and the advantage of the proposed method are further proved by experimental results.

Copyright © 2019 by ASME
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