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Open AccessArticle
A Multi-Task Network: Improving Unmanned Underwater Vehicle Self-Noise Separation via Sound Event Recognition
by
Wentao Shi
Wentao Shi 1,2,
Dong Chen
Dong Chen 2,
Fenghua Tian
Fenghua Tian 3,
Shuxun Liu
Shuxun Liu 2 and
Lianyou Jing
Lianyou Jing 1,*
1
Ocean Institute, Northwestern Polytechnical University, Taicang 215400, China
2
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
3
Xi’an Precision Machinery Research Institute, Xi’an 710077, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2024, 12(9), 1563; https://doi.org/10.3390/jmse12091563 (registering DOI)
Submission received: 20 July 2024
/
Revised: 28 August 2024
/
Accepted: 4 September 2024
/
Published: 5 September 2024
Abstract
The performance of an Unmanned Underwater Vehicle (UUV) is significantly influenced by the magnitude of self-generated noise, making it a crucial factor in advancing acoustic load technologies. Effective noise management, through the identification and separation of various self-noise types, is essential for enhancing a UUV’s reception capabilities. This paper concentrates on the development of UUV self-noise separation techniques, with a particular emphasis on feature extraction and separation in multi-task learning environments. We introduce an enhancement module designed to leverage noise categorization for improved network efficiency. Furthermore, we propose a neural network-based multi-task framework for the identification and separation of self-noise, the efficacy of which is substantiated by experimental trials conducted in a lake setting. The results demonstrate that our network outperforms the Conv-tasnet baseline, achieving a 0.99 dB increase in Signal-to-Interference-plus-Noise Ratio (SINR) and a 0.05 enhancement in the recognized energy ratio.
Share and Cite
MDPI and ACS Style
Shi, W.; Chen, D.; Tian, F.; Liu, S.; Jing, L.
A Multi-Task Network: Improving Unmanned Underwater Vehicle Self-Noise Separation via Sound Event Recognition. J. Mar. Sci. Eng. 2024, 12, 1563.
https://doi.org/10.3390/jmse12091563
AMA Style
Shi W, Chen D, Tian F, Liu S, Jing L.
A Multi-Task Network: Improving Unmanned Underwater Vehicle Self-Noise Separation via Sound Event Recognition. Journal of Marine Science and Engineering. 2024; 12(9):1563.
https://doi.org/10.3390/jmse12091563
Chicago/Turabian Style
Shi, Wentao, Dong Chen, Fenghua Tian, Shuxun Liu, and Lianyou Jing.
2024. "A Multi-Task Network: Improving Unmanned Underwater Vehicle Self-Noise Separation via Sound Event Recognition" Journal of Marine Science and Engineering 12, no. 9: 1563.
https://doi.org/10.3390/jmse12091563
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