Near-field acoustic reconstruction method based on three-dimensional N-shaped convolution neural network and frequency focal-KH regularization

JI Yuyang; WANG Deyu

doi:10.19693/j.issn.1673-3185.03127

Volume 18 Issue 6

Dec. 2023

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Chinese Journal of Ship Research > 2023 > 18(6): 186-196. > DOI: 10.19693/j.issn.1673-3185.03127 CSTR: 32390.14.j.issn.1673-3185.03127

JI Y Y, WANG D Y. Near-field acoustic reconstruction method based on three-dimensional N-shaped convolution neural network and frequency focal-KH regularization [J]. Chinese Journal of Ship Research, 2023, 18(6): 186–196. DOI: 10.19693/j.issn.1673-3185.03127

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Near-field acoustic reconstruction method based on three-dimensional N-shaped convolution neural network and frequency focal-KH regularization

JI Yuyang^{1, 2,},
WANG Deyu^{1, 2, ,}

1.
State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2.
Institute of Marine Equipment, Shanghai Jiao Tong University, Shanghai 200240, China

More Information

Received Date: October 11, 2022
Revised Date: December 21, 2022
Available Online: December 25, 2022
Published Date: December 06, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

Abstract

Objectives
Low sampling rates on reconstruction surfaces cause high reconstruction error in near-field acoustic holography. Therefore, a deep learning-based approach which is applicable to planar sound sources and high-precision reconstruction with low sampling rates is put forward.
Methods
A three-dimensional N-shaped convolution neural network for near-field acoustic reconstruction is established to extract features in the frequency dimension in order to make up for sparse sampling in the spatial dimension. A frequency focal mechanism, namely an adaptive frequency weight focus mechanism, is put forward to improve reconstruction precision in the natural frequency and high frequency. Moreover, this paper also raises frequency-scaled focal loss and frequency-scaled focal Kirchhoff–Helmholtz （KH） loss, which are considered regularization. To validate the proposed methods, datasets are created with COMSOL Multiphysics and Matlab.
Results
The mean error range of 100–2 000 Hz of the algorithm proposed in this paper is only 4.96%, higher than those of SRCNN and PV-NN.
Conclusions
The proposed method is verified as having the potential to reconstruct the accurate velocity fields of sound sources under low sampling rates.

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References

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Near-field acoustic reconstruction method based on three-dimensional N-shaped convolution neural network and frequency focal-KH regularization

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