Dual-stage learning framework for underwater acoustic target recognition with cross-attention mechanism and audio-guided contrastive learning
Underwater acoustic target recognition is crucial for marine exploration and environmental monitoring. However, the redundancy in raw time-domain signals and the challenges in effectively integrating time-frequency representations with audio features hinder current methods. To address these challeng...
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| Published in: | Neurocomputing (Amsterdam) Vol. 652; p. 131101 |
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| Format: | Journal Article |
| Language: | English |
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01.11.2025
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| ISSN: | 0925-2312 |
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| Abstract | Underwater acoustic target recognition is crucial for marine exploration and environmental monitoring. However, the redundancy in raw time-domain signals and the challenges in effectively integrating time-frequency representations with audio features hinder current methods. To address these challenges, we introduce a dual-stage learning framework, named audio-guided cross-attention dual-stage network for underwater acoustic target recognition (ACDN-UATR). The approach combines a cross-attention mechanism with audio-guided contrastive learning to improve recognition accuracy. In the first stage, a masked autoencoder (MAE) is used to learn and reconstruct time-frequency features, while a cross-attention mechanism efficiently fuses features from different spectrograms. In the second stage, contrastive learning is employed to align features extracted from time-frequency representations and raw audio signals, enhancing feature consistency and recognition robustness. Experimental results demonstrate that ACDN-UATR effectively integrates both time-frequency and audio-based features, achieving high recognition accuracy on the ShipsEar dataset.
•Novel dual-stage learning framework (ACDN-UATR) proposed for UATR.•Cross-attention MAE fuses complementary Mel and CQT spectrogram features.•Audio-guided contrastive learning aligns multimodal acoustic features.•Integrates time-frequency representation learning with raw audio analysis.•Achieves superior underwater target recognition accuracy on ShipsEar dataset. |
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| AbstractList | Underwater acoustic target recognition is crucial for marine exploration and environmental monitoring. However, the redundancy in raw time-domain signals and the challenges in effectively integrating time-frequency representations with audio features hinder current methods. To address these challenges, we introduce a dual-stage learning framework, named audio-guided cross-attention dual-stage network for underwater acoustic target recognition (ACDN-UATR). The approach combines a cross-attention mechanism with audio-guided contrastive learning to improve recognition accuracy. In the first stage, a masked autoencoder (MAE) is used to learn and reconstruct time-frequency features, while a cross-attention mechanism efficiently fuses features from different spectrograms. In the second stage, contrastive learning is employed to align features extracted from time-frequency representations and raw audio signals, enhancing feature consistency and recognition robustness. Experimental results demonstrate that ACDN-UATR effectively integrates both time-frequency and audio-based features, achieving high recognition accuracy on the ShipsEar dataset.
•Novel dual-stage learning framework (ACDN-UATR) proposed for UATR.•Cross-attention MAE fuses complementary Mel and CQT spectrogram features.•Audio-guided contrastive learning aligns multimodal acoustic features.•Integrates time-frequency representation learning with raw audio analysis.•Achieves superior underwater target recognition accuracy on ShipsEar dataset. |
| ArticleNumber | 131101 |
| Author | Xu, Jing Zhao, Lyufang Liu, Yuanxin Zhao, Rongyao Liu, Feng Li, Daihui Shen, Tongsheng |
| Author_xml | – sequence: 1 givenname: Rongyao surname: Zhao fullname: Zhao, Rongyao email: 12434048@zju.edu.cn organization: Ocean College, Zhejiang University, Zhoushan, 316021, China – sequence: 2 givenname: Feng surname: Liu fullname: Liu, Feng email: liufeng_cv@126.com organization: National Innovation Institute of Defense Technology, PLA Academy of Military Science, Beijing, 100071, China – sequence: 3 givenname: Lyufang surname: Zhao fullname: Zhao, Lyufang email: 12034052@zju.edu.cn organization: Ocean College, Zhejiang University, Zhoushan, 316021, China – sequence: 4 givenname: Daihui surname: Li fullname: Li, Daihui email: lidh_ai@sina.com organization: National Innovation Institute of Defense Technology, PLA Academy of Military Science, Beijing, 100071, China – sequence: 5 givenname: Jing surname: Xu fullname: Xu, Jing email: jxu-optics@zju.edu.cn organization: Ocean College, Zhejiang University, Zhoushan, 316021, China – sequence: 6 givenname: Yuanxin surname: Liu fullname: Liu, Yuanxin email: bce18gru@163.com organization: National Innovation Institute of Defense Technology, PLA Academy of Military Science, Beijing, 100071, China – sequence: 7 givenname: Tongsheng orcidid: 0009-0006-6665-9299 surname: Shen fullname: Shen, Tongsheng email: submit4z@163.com organization: Ocean College, Zhejiang University, Zhoushan, 316021, China |
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| Cites_doi | 10.1121/10.0026598 10.1016/j.oceaneng.2022.112626 10.1016/j.inffus.2017.02.007 10.1016/j.oceaneng.2022.112863 10.1121/10.0015053 10.1155/2018/6593037 10.1016/j.eswa.2024.123431 10.3390/s19051104 10.1016/j.oceaneng.2019.04.013 10.3390/e20120990 10.1016/j.oceaneng.2024.117252 10.1109/TASLP.2024.3358719 10.1016/j.apacoust.2016.06.008 10.1016/j.oceaneng.2024.120049 10.1109/79.689583 10.1016/j.apacoust.2021.107989 10.1109/LGRS.2020.3029584 10.1016/j.knosys.2022.110119 |
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| Keywords | Underwater acoustic target recognition Masked autoencoder Dual-stage learning Contrastive learning Cross-attention mechanism |
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| References | Van der Maaten, Hinton (bib0185) 2008; 9 Fu, Nie, Wei, Zhang (bib0120) 2024; 63 Feng, Zhu, Ma (bib0085) 2024; 32 Kamal, Chandran, Supriya (bib0115) 2021; 24 Khalilabadi (bib0040) 2023; 8 Lian, Xu, Wan, Li (bib0025) 2017 Vaccaro (bib0010) 1998; 15 Mohammed, Hariharan, Kamal (bib0015) 2018; 2018 Dong, Fu, Zou, Zhao, Miao, Shen (bib0080) 2025; 318 Xie, Ren, Xu (bib0065) 2024; 249 He, Zhang, Ren, Sun (bib0190) 2016 Tian, Chen, Fu, Zhou (bib0125) 2023; 260 Santos-Domínguez, Torres-Guijarro, Cardenal-López, Pena-Gimenez (bib0170) 2016; 113 Gong, Chung, Glass (bib0145) 2021 Vaswani (bib0155) 2017 Khishe, Mohammadi (bib0045) 2019; 181 Zhao, Xie, Xu, Sun (bib0135) 2017; 38 Chen, Du, Quan, Zhou (bib0070) 2019; vol. 283 He, Chen, Xie, Li, Dollár, Girshick (bib0140) 2022 Xie, Xu, Ren, Li (bib0060) 2024; 156 Doan, Huynh-The, Kim (bib0095) 2020; 19 Chen, Liu, Li, Shen, Zhao (bib0055) 2022 Chen, Fan, Panda (bib0150) 2021 Oord, Li, Vinyals (bib0165) 2018 Hummel, van der Mei, Bhulai (bib0030) 2024; 298 Gong, Lai, Chung, Glass (bib0205) 2022; vol. 36 Liu, Shen, Luo, Zhao, Guo (bib0035) 2021; 178 Chen, Liu, Li, Shen, Zhao (bib0090) 2022 Xie, Ren, Xu (bib0130) 2022; 152 Simonyan (bib0195) 2014 Gazneli, Zimerman, Ridnik, Sharir, Noy (bib0160) 2022 Kuzin, Statsenko, Smirnova (bib0050) 2022 Smith, Rigby (bib0005) 2022; 266 Loshchilov (bib0175) 2017 Dosovitskiy (bib0200) 2020 Xiaoping, Jinsheng, Yuan (bib0110) 2021 Smith, Topin (bib0180) 2019; vol. 11006 Tong, Zhang, Ge (bib0020) 2020 Yang, Li, Shen, Xu (bib0100) 2019; 19 Xie, Ren, Xu (bib0075) 2022; 265 Shen, Yang, Li, Xu, Sheng (bib0105) 2018; 20 Van der Maaten (10.1016/j.neucom.2025.131101_bib0185) 2008; 9 Fu (10.1016/j.neucom.2025.131101_bib0120) 2024; 63 Loshchilov (10.1016/j.neucom.2025.131101_bib0175) Chen (10.1016/j.neucom.2025.131101_bib0070) 2019; vol. 283 Chen (10.1016/j.neucom.2025.131101_bib0150) 2021 Shen (10.1016/j.neucom.2025.131101_bib0105) 2018; 20 Chen (10.1016/j.neucom.2025.131101_bib0090) 2022 Tian (10.1016/j.neucom.2025.131101_bib0125) 2023; 260 Gazneli (10.1016/j.neucom.2025.131101_bib0160) Zhao (10.1016/j.neucom.2025.131101_bib0135) 2017; 38 Chen (10.1016/j.neucom.2025.131101_bib0055) 2022 Mohammed (10.1016/j.neucom.2025.131101_bib0015) 2018; 2018 Kamal (10.1016/j.neucom.2025.131101_bib0115) 2021; 24 Feng (10.1016/j.neucom.2025.131101_bib0085) 2024; 32 He (10.1016/j.neucom.2025.131101_bib0140) 2022 Xiaoping (10.1016/j.neucom.2025.131101_bib0110) 2021 Doan (10.1016/j.neucom.2025.131101_bib0095) 2020; 19 Vaccaro (10.1016/j.neucom.2025.131101_bib0010) 1998; 15 Simonyan (10.1016/j.neucom.2025.131101_bib0195) Oord (10.1016/j.neucom.2025.131101_bib0165) Khalilabadi (10.1016/j.neucom.2025.131101_bib0040) 2023; 8 Xie (10.1016/j.neucom.2025.131101_bib0130) 2022; 152 Gong (10.1016/j.neucom.2025.131101_bib0145) Xie (10.1016/j.neucom.2025.131101_bib0075) 2022; 265 Liu (10.1016/j.neucom.2025.131101_bib0035) 2021; 178 Dong (10.1016/j.neucom.2025.131101_bib0080) 2025; 318 Vaswani (10.1016/j.neucom.2025.131101_bib0155) 2017 He (10.1016/j.neucom.2025.131101_bib0190) 2016 Lian (10.1016/j.neucom.2025.131101_bib0025) 2017 Yang (10.1016/j.neucom.2025.131101_bib0100) 2019; 19 Smith (10.1016/j.neucom.2025.131101_bib0005) 2022; 266 Gong (10.1016/j.neucom.2025.131101_bib0205) 2022; vol. 36 Xie (10.1016/j.neucom.2025.131101_bib0060) 2024; 156 Smith (10.1016/j.neucom.2025.131101_bib0180) 2019; vol. 11006 Kuzin (10.1016/j.neucom.2025.131101_bib0050) 2022 Tong (10.1016/j.neucom.2025.131101_bib0020) 2020 Xie (10.1016/j.neucom.2025.131101_bib0065) 2024; 249 Santos-Domínguez (10.1016/j.neucom.2025.131101_bib0170) 2016; 113 Khishe (10.1016/j.neucom.2025.131101_bib0045) 2019; 181 Hummel (10.1016/j.neucom.2025.131101_bib0030) 2024; 298 Dosovitskiy (10.1016/j.neucom.2025.131101_bib0200) |
| References_xml | – volume: 15 start-page: 21 year: 1998 end-page: 51 ident: bib0010 article-title: The past, present, and the future of underwater acoustic signal processing publication-title: IEEE Signal Process. Mag. – start-page: 357 year: 2021 end-page: 366 ident: bib0150 article-title: Crossvit: cross-attention multi-scale vision transformer for image classification publication-title: Proceedings of the IEEE/CVF international conference on computer vision – year: 2017 ident: bib0175 article-title: Decoupled weight decay regularization – volume: 19 start-page: 1 year: 2020 end-page: 5 ident: bib0095 article-title: Underwater acoustic target classification based on dense convolutional neural network publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 152 start-page: 2641 year: 2022 end-page: 2651 ident: bib0130 article-title: Underwater-art: expanding information perspectives with text templates for underwater acoustic target recognition publication-title: J. Acoustical Soc. Am. – volume: 8 start-page: 10 year: 2023 end-page: 15 ident: bib0040 article-title: Underwater ship-radiated acoustic noise recognition based on mel-spectrogram and convolutional neural network publication-title: Int. J. Coast. Offshore Environ. Eng. (Ijcoe) – volume: 38 start-page: 43 year: 2017 end-page: 54 ident: bib0135 article-title: Multi-view learning overview: recent progress and new challenges publication-title: Inf. Fusion – volume: 181 start-page: 98 year: 2019 end-page: 108 ident: bib0045 article-title: Passive sonar target classification using multi-layer perceptron trained by salp swarm algorithm publication-title: Ocean Eng. – volume: 19 start-page: 1104 year: 2019 ident: bib0100 article-title: A deep convolutional neural network inspired by auditory perception for underwater acoustic target recognition publication-title: Sensors – year: 2014 ident: bib0195 article-title: Very deep convolutional networks for large-scale image recognition – start-page: 1 year: 2020 end-page: 4 ident: bib0020 article-title: Classification and recognition of underwater target based on mfcc feature extraction publication-title: 2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) – volume: 260 year: 2023 ident: bib0125 article-title: Joint learning model for underwater acoustic target recognition publication-title: Know Based Syst. – volume: 265 year: 2022 ident: bib0075 article-title: Adaptive ship-radiated noise recognition with learnable fine-grained wavelet transform publication-title: Ocean Eng. – start-page: 23 year: 2022 end-page: 28 ident: bib0090 article-title: Underwater acoustic target classification with joint learning framework and data augmentation publication-title: 2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD) – year: 2018 ident: bib0165 article-title: Representation learning with contrastive predictive coding – volume: vol. 283 year: 2019 ident: bib0070 article-title: Underwater target recognition method based on convolution residual network publication-title: MATEC Web of Conferences – volume: 266 year: 2022 ident: bib0005 article-title: Underwater radiated noise from marine vessels: a review of noise reduction methods and technology publication-title: Ocean Eng. – year: 2021 ident: bib0145 article-title: Ast: audio spectrogram transformer – volume: 9 year: 2008 ident: bib0185 article-title: Visualizing data using t-sne. publication-title: J. Mach. Learn. Res. – volume: 178 year: 2021 ident: bib0035 article-title: Underwater target recognition using convolutional recurrent neural networks with 3-d mel-spectrogram and data augmentation publication-title: Appl. Acoust. – volume: vol. 11006 start-page: 369 year: 2019 end-page: 386 ident: bib0180 article-title: Super-convergence: very fast training of neural networks using large learning rates publication-title: Artificial intelligence and machine learning for multi-domain operations applications – volume: 113 start-page: 64 year: 2016 end-page: 69 ident: bib0170 article-title: Shipsear: An underwater vessel noise database publication-title: Appl. Acoust. – volume: 156 start-page: 299 year: 2024 end-page: 313 ident: bib0060 article-title: Adversarial multi-task underwater acoustic target recognition: toward robustness against various influential factors publication-title: J. Acoustical Soc. Am. – volume: 298 year: 2024 ident: bib0030 article-title: A survey on machine learning in ship radiated noise publication-title: Ocean Eng. – volume: 24 start-page: 860 year: 2021 end-page: 871 ident: bib0115 article-title: Passive sonar automated target classifier for shallow waters using end-to-end learnable deep convolutional lstms publication-title: Eng. Sci. Technol. Int. J. – volume: 32 start-page: 1365 year: 2024 end-page: 1379 ident: bib0085 article-title: Masking hierarchical tokens for underwater acoustic target recognition with self-supervised learning publication-title: IEEE/ACM Trans. Audio, Speech, Language Process. – volume: 20 start-page: 990 year: 2018 ident: bib0105 article-title: Auditory inspired convolutional neural networks for ship type classification with raw hydrophone data publication-title: Entropy – start-page: 23 year: 2022 end-page: 28 ident: bib0055 article-title: Underwater acoustic target classification with joint learning framework and data augmentation publication-title: 2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD) – volume: 318 year: 2025 ident: bib0080 article-title: Caf-vit: a cross-attention based transformer network for underwater acoustic target recognition publication-title: Ocean Eng. – start-page: 258 year: 2017 end-page: 262 ident: bib0025 article-title: Underwater acoustic target classification based on modified gfcc features publication-title: 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) – volume: 249 year: 2024 ident: bib0065 article-title: Unraveling complex data diversity in underwater acoustic target recognition through convolution-based mixture of experts publication-title: Expert Syst. Appl. – volume: 2018 year: 2018 ident: bib0015 article-title: A gtcc-based underwater hmm target classifier with fading channel compensation publication-title: J. Sens. – start-page: 770 year: 2016 end-page: 778 ident: bib0190 article-title: Deep residual learning for image recognition publication-title: Proceedings of the IEEE conference on computer vision and pattern recognition – start-page: 16000 year: 2022 end-page: 16009 ident: bib0140 article-title: Masked autoencoders are scalable vision learners publication-title: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition – year: 2017 ident: bib0155 article-title: Attention is all you need publication-title: Adv. Neural Inf. Process. Syst. – volume: vol. 36 start-page: 10699 year: 2022 end-page: 10709 ident: bib0205 article-title: Ssast: self-supervised audio spectrogram transformer publication-title: Proceedings of the AAAI Conference on Artificial Intelligence – year: 2020 ident: bib0200 article-title: An image is worth 16x16 words: transformers for image recognition at scale – start-page: 87 year: 2022 end-page: 90 ident: bib0050 article-title: Automated sea vehicle classification system based on neural network publication-title: 2022 International Conference on Ocean Studies (ICOS) – start-page: 1048 year: 2021 end-page: 1051 ident: bib0110 article-title: A new deep learning method for underwater target recognition based on one-dimensional time-domain signals publication-title: 2021 OES China Ocean Acoustics (COA) – year: 2022 ident: bib0160 article-title: End-to-end audio strikes back: boosting augmentations towards an efficient audio classification network – volume: 63 start-page: 1 year: 2024 end-page: 14 ident: bib0120 article-title: Constructing a multi-modal based underwater acoustic target recognition method with a pre-trained language-audio model publication-title: IEEE Transactions On Geoscience And Remote Sensing – start-page: 1 year: 2020 ident: 10.1016/j.neucom.2025.131101_bib0020 article-title: Classification and recognition of underwater target based on mfcc feature extraction – volume: 156 start-page: 299 issue: 1 year: 2024 ident: 10.1016/j.neucom.2025.131101_bib0060 article-title: Adversarial multi-task underwater acoustic target recognition: toward robustness against various influential factors publication-title: J. Acoustical Soc. Am. doi: 10.1121/10.0026598 – volume: 265 year: 2022 ident: 10.1016/j.neucom.2025.131101_bib0075 article-title: Adaptive ship-radiated noise recognition with learnable fine-grained wavelet transform publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2022.112626 – volume: 38 start-page: 43 year: 2017 ident: 10.1016/j.neucom.2025.131101_bib0135 article-title: Multi-view learning overview: recent progress and new challenges publication-title: Inf. Fusion doi: 10.1016/j.inffus.2017.02.007 – ident: 10.1016/j.neucom.2025.131101_bib0195 – ident: 10.1016/j.neucom.2025.131101_bib0200 – start-page: 87 year: 2022 ident: 10.1016/j.neucom.2025.131101_bib0050 article-title: Automated sea vehicle classification system based on neural network – start-page: 23 year: 2022 ident: 10.1016/j.neucom.2025.131101_bib0090 article-title: Underwater acoustic target classification with joint learning framework and data augmentation – ident: 10.1016/j.neucom.2025.131101_bib0175 – volume: 266 year: 2022 ident: 10.1016/j.neucom.2025.131101_bib0005 article-title: Underwater radiated noise from marine vessels: a review of noise reduction methods and technology publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2022.112863 – ident: 10.1016/j.neucom.2025.131101_bib0160 – volume: 152 start-page: 2641 issue: 5 year: 2022 ident: 10.1016/j.neucom.2025.131101_bib0130 article-title: Underwater-art: expanding information perspectives with text templates for underwater acoustic target recognition publication-title: J. Acoustical Soc. Am. doi: 10.1121/10.0015053 – start-page: 1048 year: 2021 ident: 10.1016/j.neucom.2025.131101_bib0110 article-title: A new deep learning method for underwater target recognition based on one-dimensional time-domain signals – volume: 2018 year: 2018 ident: 10.1016/j.neucom.2025.131101_bib0015 article-title: A gtcc-based underwater hmm target classifier with fading channel compensation publication-title: J. Sens. doi: 10.1155/2018/6593037 – ident: 10.1016/j.neucom.2025.131101_bib0165 – volume: 8 start-page: 10 year: 2023 ident: 10.1016/j.neucom.2025.131101_bib0040 article-title: Underwater ship-radiated acoustic noise recognition based on mel-spectrogram and convolutional neural network publication-title: Int. J. Coast. Offshore Environ. Eng. (Ijcoe) – volume: 9 year: 2008 ident: 10.1016/j.neucom.2025.131101_bib0185 article-title: Visualizing data using t-sne. publication-title: J. Mach. Learn. Res. – start-page: 770 year: 2016 ident: 10.1016/j.neucom.2025.131101_bib0190 article-title: Deep residual learning for image recognition – volume: 249 year: 2024 ident: 10.1016/j.neucom.2025.131101_bib0065 article-title: Unraveling complex data diversity in underwater acoustic target recognition through convolution-based mixture of experts publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2024.123431 – volume: 19 start-page: 1104 issue: 5 year: 2019 ident: 10.1016/j.neucom.2025.131101_bib0100 article-title: A deep convolutional neural network inspired by auditory perception for underwater acoustic target recognition publication-title: Sensors doi: 10.3390/s19051104 – volume: 181 start-page: 98 year: 2019 ident: 10.1016/j.neucom.2025.131101_bib0045 article-title: Passive sonar target classification using multi-layer perceptron trained by salp swarm algorithm publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2019.04.013 – start-page: 258 year: 2017 ident: 10.1016/j.neucom.2025.131101_bib0025 article-title: Underwater acoustic target classification based on modified gfcc features – start-page: 357 year: 2021 ident: 10.1016/j.neucom.2025.131101_bib0150 article-title: Crossvit: cross-attention multi-scale vision transformer for image classification – volume: vol. 283 year: 2019 ident: 10.1016/j.neucom.2025.131101_bib0070 article-title: Underwater target recognition method based on convolution residual network – year: 2017 ident: 10.1016/j.neucom.2025.131101_bib0155 article-title: Attention is all you need publication-title: Adv. Neural Inf. Process. Syst. – volume: 20 start-page: 990 issue: 12 year: 2018 ident: 10.1016/j.neucom.2025.131101_bib0105 article-title: Auditory inspired convolutional neural networks for ship type classification with raw hydrophone data publication-title: Entropy doi: 10.3390/e20120990 – volume: 298 year: 2024 ident: 10.1016/j.neucom.2025.131101_bib0030 article-title: A survey on machine learning in ship radiated noise publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2024.117252 – start-page: 23 year: 2022 ident: 10.1016/j.neucom.2025.131101_bib0055 article-title: Underwater acoustic target classification with joint learning framework and data augmentation – volume: 32 start-page: 1365 year: 2024 ident: 10.1016/j.neucom.2025.131101_bib0085 article-title: Masking hierarchical tokens for underwater acoustic target recognition with self-supervised learning publication-title: IEEE/ACM Trans. Audio, Speech, Language Process. doi: 10.1109/TASLP.2024.3358719 – volume: 113 start-page: 64 year: 2016 ident: 10.1016/j.neucom.2025.131101_bib0170 article-title: Shipsear: An underwater vessel noise database publication-title: Appl. Acoust. doi: 10.1016/j.apacoust.2016.06.008 – volume: 63 start-page: 1 year: 2024 ident: 10.1016/j.neucom.2025.131101_bib0120 article-title: Constructing a multi-modal based underwater acoustic target recognition method with a pre-trained language-audio model publication-title: IEEE Transactions On Geoscience And Remote Sensing – volume: 318 year: 2025 ident: 10.1016/j.neucom.2025.131101_bib0080 article-title: Caf-vit: a cross-attention based transformer network for underwater acoustic target recognition publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2024.120049 – volume: 15 start-page: 21 issue: 4 year: 1998 ident: 10.1016/j.neucom.2025.131101_bib0010 article-title: The past, present, and the future of underwater acoustic signal processing publication-title: IEEE Signal Process. Mag. doi: 10.1109/79.689583 – start-page: 16000 year: 2022 ident: 10.1016/j.neucom.2025.131101_bib0140 article-title: Masked autoencoders are scalable vision learners – volume: 178 year: 2021 ident: 10.1016/j.neucom.2025.131101_bib0035 article-title: Underwater target recognition using convolutional recurrent neural networks with 3-d mel-spectrogram and data augmentation publication-title: Appl. Acoust. doi: 10.1016/j.apacoust.2021.107989 – volume: 24 start-page: 860 issue: 4 year: 2021 ident: 10.1016/j.neucom.2025.131101_bib0115 article-title: Passive sonar automated target classifier for shallow waters using end-to-end learnable deep convolutional lstms publication-title: Eng. Sci. Technol. Int. J. – volume: vol. 36 start-page: 10699 year: 2022 ident: 10.1016/j.neucom.2025.131101_bib0205 article-title: Ssast: self-supervised audio spectrogram transformer – volume: 19 start-page: 1 year: 2020 ident: 10.1016/j.neucom.2025.131101_bib0095 article-title: Underwater acoustic target classification based on dense convolutional neural network publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2020.3029584 – ident: 10.1016/j.neucom.2025.131101_bib0145 – volume: 260 year: 2023 ident: 10.1016/j.neucom.2025.131101_bib0125 article-title: Joint learning model for underwater acoustic target recognition publication-title: Know Based Syst. doi: 10.1016/j.knosys.2022.110119 – volume: vol. 11006 start-page: 369 year: 2019 ident: 10.1016/j.neucom.2025.131101_bib0180 article-title: Super-convergence: very fast training of neural networks using large learning rates |
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| Title | Dual-stage learning framework for underwater acoustic target recognition with cross-attention mechanism and audio-guided contrastive learning |
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