Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network
The presence of natural ambient noise interferes with the system for locating and identifying underwater targets. This paper suggests that a Dual-Path Transformation Network (DPTN) reduces ambient noise in underwater acoustic signals. First, the input acoustic signals’ higher-order non-li...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9963926/ |
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| author | Yongqiang Song Feng Liu Tongsheng Shen |
| author_facet | Yongqiang Song Feng Liu Tongsheng Shen |
| author_sort | Yongqiang Song |
| collection | DOAJ |
| description | The presence of natural ambient noise interferes with the system for locating and identifying underwater targets. This paper suggests that a Dual-Path Transformation Network (DPTN) reduces ambient noise in underwater acoustic signals. First, the input acoustic signals’ higher-order non-linear features are extracted using a multi-scale convolutional encoder neural network. Second, sub-vectors with the same length are created according to the time dimension from the higher-order non-linear features. The sub-vectors are stitched together to form a three-dimensional tensor. Third, a neural network transformer based on the feed-forward network is constructed. Further, to capture long-term series features and separate the target signal from the noisy signals, the three-dimensional tensor is used as the input of the transformer-based masking network. Finally, overlap-add and transpose are used to obtain discernible target signals. The experimental results verify the effectiveness of the proposed underwater acoustic signal denoising algorithm and demonstrate that the proposed DPRN method can obtain higher output signal-to-noise ratio (SNR) and the scale-invariant signal-to-noise ratio (SI-SNR) compared with the other classical algorithms. |
| format | Article |
| id | doaj-art-febdc4b3ecd446c7aecca1cfc8bb5adb |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-febdc4b3ecd446c7aecca1cfc8bb5adb2025-08-20T03:21:26ZengIEEEIEEE Access2169-35362024-01-0112814838149410.1109/ACCESS.2022.32247529963926Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer NetworkYongqiang Song0https://orcid.org/0000-0001-5193-5314Feng Liu1Tongsheng Shen2National Innovation Institute of Defense Technology, Beijing, ChinaChinese Academy of Military Sciences, Beijing, ChinaChinese Academy of Military Sciences, Beijing, ChinaThe presence of natural ambient noise interferes with the system for locating and identifying underwater targets. This paper suggests that a Dual-Path Transformation Network (DPTN) reduces ambient noise in underwater acoustic signals. First, the input acoustic signals’ higher-order non-linear features are extracted using a multi-scale convolutional encoder neural network. Second, sub-vectors with the same length are created according to the time dimension from the higher-order non-linear features. The sub-vectors are stitched together to form a three-dimensional tensor. Third, a neural network transformer based on the feed-forward network is constructed. Further, to capture long-term series features and separate the target signal from the noisy signals, the three-dimensional tensor is used as the input of the transformer-based masking network. Finally, overlap-add and transpose are used to obtain discernible target signals. The experimental results verify the effectiveness of the proposed underwater acoustic signal denoising algorithm and demonstrate that the proposed DPRN method can obtain higher output signal-to-noise ratio (SNR) and the scale-invariant signal-to-noise ratio (SI-SNR) compared with the other classical algorithms.https://ieeexplore.ieee.org/document/9963926/Deep learningunderwater acousticsdual-path transformer network |
| spellingShingle | Yongqiang Song Feng Liu Tongsheng Shen Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network IEEE Access Deep learning underwater acoustics dual-path transformer network |
| title | Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network |
| title_full | Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network |
| title_fullStr | Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network |
| title_full_unstemmed | Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network |
| title_short | Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network |
| title_sort | method of underwater acoustic signal denoising based on dual path transformer network |
| topic | Deep learning underwater acoustics dual-path transformer network |
| url | https://ieeexplore.ieee.org/document/9963926/ |
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