DWSTr: a hybrid framework for ship-radiated noise recognition
The critical nature of passive ship-radiated noise recognition for military and economic security is well-established, yet its advancement faces significant obstacles due to the complex marine environment. The challenges include natural sound interference and signal distortion, complicating the extr...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2024.1334057/full |
| _version_ | 1797306971231092736 |
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| author | Yan Wang Hao Zhang Wei Huang Manli Zhou Yong Gao Yuan An Huifeng Jiao Huifeng Jiao |
| author_facet | Yan Wang Hao Zhang Wei Huang Manli Zhou Yong Gao Yuan An Huifeng Jiao Huifeng Jiao |
| author_sort | Yan Wang |
| collection | DOAJ |
| description | The critical nature of passive ship-radiated noise recognition for military and economic security is well-established, yet its advancement faces significant obstacles due to the complex marine environment. The challenges include natural sound interference and signal distortion, complicating the extraction of key acoustic features and ship type identification. Addressing these issues, this study introduces DWSTr, a novel method combining a depthwise separable convolutional neural network with a Transformer architecture. This approach effectively isolates local acoustic features and captures global dependencies, enhancing robustness against environmental interferences and signal variability. Validated by experimental results on the ShipsEar dataset, DWSTr demonstrated a notable 96.5\% recognition accuracy, underscoring its efficacy in accurate ship classification amidst challenging conditions. The integration of these advanced neural architectures not only surmounts existing barriers in noise recognition but also offers computational efficiency for real-time analysis, marking a significant advancement in passive acoustic monitoring and its application in strategic and economic contexts. |
| first_indexed | 2024-03-08T00:50:34Z |
| format | Article |
| id | doaj.art-003966a7666d4e93857e6915b2c35275 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-03-08T00:50:34Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-003966a7666d4e93857e6915b2c352752024-02-15T04:36:47ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452024-02-011110.3389/fmars.2024.13340571334057DWSTr: a hybrid framework for ship-radiated noise recognitionYan Wang0Hao Zhang1Wei Huang2Manli Zhou3Yong Gao4Yuan An5Huifeng Jiao6Huifeng Jiao7Department of Electrical Engineering, Ocean University of China, Qingdao, ChinaDepartment of Electrical Engineering, Ocean University of China, Qingdao, ChinaDepartment of Electrical Engineering, Ocean University of China, Qingdao, ChinaDepartment of Electrical Engineering, Ocean University of China, Qingdao, ChinaDepartment of Electrical Engineering, Ocean University of China, Qingdao, ChinaDepartment of Electrical Engineering, Ocean University of China, Qingdao, ChinaState Key Laboratory of Deep-sea Manned Vehicles, China Ship Scientific Research Center, Wuxi, ChinaTaihu Laboratory of Deepsea Technological Science, Wuxi, ChinaThe critical nature of passive ship-radiated noise recognition for military and economic security is well-established, yet its advancement faces significant obstacles due to the complex marine environment. The challenges include natural sound interference and signal distortion, complicating the extraction of key acoustic features and ship type identification. Addressing these issues, this study introduces DWSTr, a novel method combining a depthwise separable convolutional neural network with a Transformer architecture. This approach effectively isolates local acoustic features and captures global dependencies, enhancing robustness against environmental interferences and signal variability. Validated by experimental results on the ShipsEar dataset, DWSTr demonstrated a notable 96.5\% recognition accuracy, underscoring its efficacy in accurate ship classification amidst challenging conditions. The integration of these advanced neural architectures not only surmounts existing barriers in noise recognition but also offers computational efficiency for real-time analysis, marking a significant advancement in passive acoustic monitoring and its application in strategic and economic contexts.https://www.frontiersin.org/articles/10.3389/fmars.2024.1334057/fullship-radiated noiseunderwater acoustic target recognitiondeep learningDWS-Transformer collaborative deep learning networkdepthwise separable convolution |
| spellingShingle | Yan Wang Hao Zhang Wei Huang Manli Zhou Yong Gao Yuan An Huifeng Jiao Huifeng Jiao DWSTr: a hybrid framework for ship-radiated noise recognition Frontiers in Marine Science ship-radiated noise underwater acoustic target recognition deep learning DWS-Transformer collaborative deep learning network depthwise separable convolution |
| title | DWSTr: a hybrid framework for ship-radiated noise recognition |
| title_full | DWSTr: a hybrid framework for ship-radiated noise recognition |
| title_fullStr | DWSTr: a hybrid framework for ship-radiated noise recognition |
| title_full_unstemmed | DWSTr: a hybrid framework for ship-radiated noise recognition |
| title_short | DWSTr: a hybrid framework for ship-radiated noise recognition |
| title_sort | dwstr a hybrid framework for ship radiated noise recognition |
| topic | ship-radiated noise underwater acoustic target recognition deep learning DWS-Transformer collaborative deep learning network depthwise separable convolution |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2024.1334057/full |
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