A Multi-Task Network: Improving Unmanned Underwater Vehicle Self-Noise Separation via Sound Event Recognition

Shi, Wentao; Chen, Dong; Tian, Fenghua; Liu, Shuxun; Jing, Lianyou

doi:10.3390/jmse12091563

Open AccessArticle

A Multi-Task Network: Improving Unmanned Underwater Vehicle Self-Noise Separation via Sound Event Recognition

by

Wentao Shi

^1,2

,

Dong Chen

²,

Fenghua Tian

³,

Shuxun Liu

² and

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

Submission received: 20 July 2024 / Revised: 28 August 2024 / Accepted: 4 September 2024 / Published: 5 September 2024

(This article belongs to the Section Ocean Engineering)

Download

Browse Figures

Versions Notes

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.

Keywords: self-noise; identification and separation; sound event recognition; neural network; multi-task network

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

APA Style

Shi, W., Chen, D., Tian, F., Liu, S., & Jing, L. (2024). A Multi-Task Network: Improving Unmanned Underwater Vehicle Self-Noise Separation via Sound Event Recognition. Journal of Marine Science and Engineering, 12(9), 1563. https://doi.org/10.3390/jmse12091563

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

A Multi-Task Network: Improving Unmanned Underwater Vehicle Self-Noise Separation via Sound Event Recognition

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI