| Autor: |
Wentao Shi, Dong Chen, Fenghua Tian, Shuxun Liu, Lianyou Jing |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Journal of Marine Science and Engineering, Vol 12, Iss 9, p 1563 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2077-1312 |
| DOI: |
10.3390/jmse12091563 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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