Underwater Reverberation Suppression Using Wavelet Transform and Complementary Learning
Reverberation is the primary interference of active detection. Therefore, the effective suppression of reverberation is a prerequisite for reliable signal processing. Existing dereverberation methods have shown effectiveness in specific scenarios. However, they often struggle to exploit the distinct...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Oceans |
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| Online Access: | https://www.mdpi.com/2673-1924/6/2/36 |
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| author | Jiajie Liu Qunfei Zhang Xiaodong Cui Chencong Tang Zijun Pu |
| author_facet | Jiajie Liu Qunfei Zhang Xiaodong Cui Chencong Tang Zijun Pu |
| author_sort | Jiajie Liu |
| collection | DOAJ |
| description | Reverberation is the primary interference of active detection. Therefore, the effective suppression of reverberation is a prerequisite for reliable signal processing. Existing dereverberation methods have shown effectiveness in specific scenarios. However, they often struggle to exploit the distinction between target echo and reverberation, especially in complex, dynamically changing underwater environments. This paper proposes a novel dereverberation network, ERCL-AttentionNet (Echo–Reverberation Complementary Learning Attention Network). We use the Continuous Wavelet Transform (CWT) to extract time–frequency features from the received signal, effectively balancing the time and frequency resolution. The real and imaginary parts of the time–frequency matrix are combined to generate attention representations, which are processed by the network. The network architecture consists of two complementary UNet models sharing the same encoder. These models independently learn target echo and reverberation features to reconstruct the target echo. An attention mechanism further enhances performance by focusing on target information and suppressing irrelevant disturbances in complex environments. Experimental results demonstrate that our method achieves a higher Peak-to-Average Signal-to-Reverberation Ratio (PSRR), Structural Similarity Index (SSIM), and Peak-to-Average Ratio (PAR) of cross-correlation while effectively preserving key time–frequency features, compared to traditional methods such as autoregressive (AR) and singular value decomposition (SVD). |
| format | Article |
| id | doaj-art-aaf87a77283d4e99bc296b2af63a4684 |
| institution | Kabale University |
| issn | 2673-1924 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Oceans |
| spelling | doaj-art-aaf87a77283d4e99bc296b2af63a46842025-08-20T03:29:35ZengMDPI AGOceans2673-19242025-06-01623610.3390/oceans6020036Underwater Reverberation Suppression Using Wavelet Transform and Complementary LearningJiajie Liu0Qunfei Zhang1Xiaodong Cui2Chencong Tang3Zijun Pu4School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaReverberation is the primary interference of active detection. Therefore, the effective suppression of reverberation is a prerequisite for reliable signal processing. Existing dereverberation methods have shown effectiveness in specific scenarios. However, they often struggle to exploit the distinction between target echo and reverberation, especially in complex, dynamically changing underwater environments. This paper proposes a novel dereverberation network, ERCL-AttentionNet (Echo–Reverberation Complementary Learning Attention Network). We use the Continuous Wavelet Transform (CWT) to extract time–frequency features from the received signal, effectively balancing the time and frequency resolution. The real and imaginary parts of the time–frequency matrix are combined to generate attention representations, which are processed by the network. The network architecture consists of two complementary UNet models sharing the same encoder. These models independently learn target echo and reverberation features to reconstruct the target echo. An attention mechanism further enhances performance by focusing on target information and suppressing irrelevant disturbances in complex environments. Experimental results demonstrate that our method achieves a higher Peak-to-Average Signal-to-Reverberation Ratio (PSRR), Structural Similarity Index (SSIM), and Peak-to-Average Ratio (PAR) of cross-correlation while effectively preserving key time–frequency features, compared to traditional methods such as autoregressive (AR) and singular value decomposition (SVD).https://www.mdpi.com/2673-1924/6/2/36reverberation suppressioncomplementary learningContinuous Wavelet Transformattentional mechanisms |
| spellingShingle | Jiajie Liu Qunfei Zhang Xiaodong Cui Chencong Tang Zijun Pu Underwater Reverberation Suppression Using Wavelet Transform and Complementary Learning Oceans reverberation suppression complementary learning Continuous Wavelet Transform attentional mechanisms |
| title | Underwater Reverberation Suppression Using Wavelet Transform and Complementary Learning |
| title_full | Underwater Reverberation Suppression Using Wavelet Transform and Complementary Learning |
| title_fullStr | Underwater Reverberation Suppression Using Wavelet Transform and Complementary Learning |
| title_full_unstemmed | Underwater Reverberation Suppression Using Wavelet Transform and Complementary Learning |
| title_short | Underwater Reverberation Suppression Using Wavelet Transform and Complementary Learning |
| title_sort | underwater reverberation suppression using wavelet transform and complementary learning |
| topic | reverberation suppression complementary learning Continuous Wavelet Transform attentional mechanisms |
| url | https://www.mdpi.com/2673-1924/6/2/36 |
| work_keys_str_mv | AT jiajieliu underwaterreverberationsuppressionusingwavelettransformandcomplementarylearning AT qunfeizhang underwaterreverberationsuppressionusingwavelettransformandcomplementarylearning AT xiaodongcui underwaterreverberationsuppressionusingwavelettransformandcomplementarylearning AT chencongtang underwaterreverberationsuppressionusingwavelettransformandcomplementarylearning AT zijunpu underwaterreverberationsuppressionusingwavelettransformandcomplementarylearning |