Inverse Patch Transfer Function With Fast Iterative Shrinkage-Thresholding Algorithm as a Tool for Sparse Source Identification

Lingyu Lou

doi:10.1109/ACCESS.2020.2966304

2020-01-01

DOI：10.1109/ACCESS.2020.2966304

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摘要

En 中文

Inverse patch transfer functions (iPTF) method is an efficient technique for sound field reconstruction and separation of arbitrarily shaped sound sources in noisy acoustic environments. By applying Neumann boundary condition to a closed virtual cavity surrounding the source, the Helmholtz integral equation is simplified and can be solved with a Green & x2019;s function satisfying the Neumann boundary condition. However, in the identification of sparsely distributed sources, ghost sources appear in the result solved by using the iPTF method with classic Tikhonov regularization methods and influence the accuracy of identification. In the present work, an evanescent Green & x2019;s function with fast convergence of calculation is utilized, and a technique that combines the iPTF method and Fast Iterative Shrinkage-Thresholding Algorithm aimed at improving the performance for the identification of sparsely distributed source is proposed. Then double layer measurements, instead of using expensive p-u probes, are employed to acquire the normal velocity of the hologram surface. In numerical simulations, the normal velocities of two anti-phased piston sources are well reconstructed with high sparsity while a disturbing source is radiating in the sound field within the frequency band of 50 to 1000 Hz. Finally, an experiment on two baffled loudspeakers has been carried out. The results of simulations and experiments indicate that the proposed technique has obviously improved the accuracy of the iPTF method in the identification of sparsely distributed sources for the frequency band from 50 to 1000 Hz.

Keyword:

Acoustic source identification

fast iterative shrinkage-thresholding algorithm

Green's function

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期刊

IEEE Access

IF:

3.6

论文数:

9.8W

被引数:

29.4W

机构

Chongqing University

学者数:

5.1W

论文数: 4.1W

被引数: 6.0W

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