Improved Auditory Inspired Convolutional Neural Networks for Ship Type Classification

WaveNet: a generative model for raw audio

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ShipsEar: An underwater vessel noise database

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Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition

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Marine vessel classification based on passive sonar data: the cepstrum‐based approach

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Deep learning-based recognition of underwater target

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Deep learning architectures for underwater target recognition

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A Complexity-Based Approach for the Detection of Weak Signals in Ocean Ambient Noise

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The classification of underwater acoustic target signals based on wave structure and support vector machine

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Preprocessing passive sonar signals for neural classification

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Auditory Inspired Convolutional Neural Networks for Ship Type Classification with Raw Hydrophone Data

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Underwater acoustic target recognition using SVM ensemble via weighted sample and feature selection

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A wave structure based method for recognition of marine acoustic target signals

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Compression of a Deep Competitive Network Based on Mutual Information for Underwater Acoustic Targets Recognition

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Underwater Target Recognition Based on Wavelet Packet and Principal Component Analysis

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Underwater acoustic target classification and auditory feature identification based on dissimilarity evaluation

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A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance

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Deep convolution stack for waveform in underwater acoustic target recognition

Tian, S., Chen, D., Wang, H., & Liu, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-88799-z · Cited by 86 · see citing works

Joint learning model for underwater acoustic target recognition

Tian, S., Chen, D., Fu, Y., & Zhou, J. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110119 · Cited by 70 · see citing works

A survey on machine learning in ship radiated noise

Hummel, H. I., van der Mei, R., & Bhulai, S. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.117252 · Cited by 71 · see citing works

Underwater single-channel acoustic signal multitarget recognition using convolutional neural networks

Sun, Q., & Wang, K. (2022) · The Journal of the Acoustical Society of America · doi:10.1121/10.0009852 · Cited by 59 · see citing works

Underwater sound classification using learning based methods: A review

Aslam, M. A., Zhang, L., Liu, X., Irfan, M., Xu, Y., Li, N., Zhang, P., Jiangbin, Z., & Li, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124498 · Cited by 59 · see citing works

Open set recognition of underwater acoustic targets based on GRU-CAE collaborative deep learning network

Yang, H., Zheng, K., & Li, J. (2022) · Applied Acoustics · doi:10.1016/j.apacoust.2022.108774 · Cited by 42 · see citing works

Offshore ship recognition based on center frequency projection of improved EMD and KNN algorithm

Jin, S.-Y., Su, Y. X., Guo, C.-J., Fan, Y.-X., & Tao, Z.-Y. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.110076 · Cited by 36 · see citing works

Passive sonar automated target classifier for shallow waters using end-to-end learnable deep convolutional LSTMs

Kamal, S., Satheesh Chandran, C., & Supriya, M. H. (2021) · Engineering Science and Technology, an International Journal · doi:10.1016/j.jestch.2021.01.014 · Cited by 33 · see citing works

CAF-ViT: A cross-attention based Transformer network for underwater acoustic target recognition

Dong, W., Fu, J. W., Zou, N., Zhao, C., Miao, Y., & Shen, Z. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.120049 · Cited by 32 · see citing works

SFC-Sup: Robust Two-Stage Underwater Acoustic Target Recognition Method Based on Supervised Contrastive Learning

Zhu, P., Zhang, Y., Huang, Y., Lin, B., Zhu, M., Zhao, K., & Zhou, F. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3329653 · Cited by 21 · see citing works

Classifying marine mammals signal using cubic splines interpolation combining with triple loss variational auto-encoder

Bach, N. H., Vu, L. H., Nguyễn, V. Đ., & Pham, D. P. (2023) · Scientific Reports · doi:10.1038/s41598-023-47320-4 · Cited by 14 · see citing works

A Deep-Shallow Network for Passive Underwater Target Recognition

Song, G., Liu, X., Zeng, X., Luo, H., Wang, D., & Zhang, B. (2020) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-smartcity-dss50907.2020.00105 · Cited by 6 · see citing works

Efficient ship noise classification with positive incentive noise and fused features using a simple convolutional network

Xu, L., Dong, R., Zhao, Y., & Wang, R. (2023) · Scientific Reports · doi:10.1038/s41598-023-45245-6 · Cited by 4 · see citing works

Adversarial Training for Underwater Target Recognition in Complex Marine Conditions

Zeng, X., Liu, X., Song, G., Wang, D., Luo, H., & Zhang, B. (2020) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-smartcity-dss50907.2020.00151 · Cited by 4 · see citing works

Deep Learning on Active Sonar Data Using Bayesian Optimization for Hyperparameter Tuning

Berg, H. S., & Hjelmervik, K. T. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412347 · Cited by 3 · see citing works

Machine learning methods for vessel type classification with underwater acoustic data

Domingos, L. C. F. (2022) · doi:10.31414/ee.2022.d.131558

Active Deep Learning Technique for Underwater Target Recognition

Lyu, J., Jiang, L. W., Yang, C., & Wang, S. (2022) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceans47191.2022.9977055

Fuzzy Support Vector Machine Design for Tolerant Underwater Acoustic Environments

Guo, S. (2025) · International Conference on Information Communication and Signal Processing (ICICSP) · doi:10.1109/icicsp66564.2025.11338448

Auditory Inspired Convolutional Neural Networks for Ship Type Classification with Raw Hydrophone Data

Shen, S., Yang, H., Li, J., Xu, G., & Sheng, M. (2018) · Entropy · doi:10.3390/e20120990 · Cited by 55 · see citing works

Ship Type Classification by Convolutional Neural Networks with Auditory-Like Mechanisms

Shen, S., Yang, H., Yao, X., Li, J., Xu, G., & Sheng, M. (2020) · Sensors · doi:10.3390/s20010253 · Cited by 57 · see citing works

Ship Radiation Noise Identification Method Based on CNN

Zhang, C., Wang, X., Li, R., & Yu, P. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.03.234

A Lightweight Network Based on Multi-Scale Asymmetric Convolutional Neural Networks with Attention Mechanism for Ship-Radiated Noise Classification

Yan, C., Yan, S., Yao, T., Yu, Y. F., Pan, G., Liu, L., Wang, M., & Bai, J. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12010130 · Cited by 17 · see citing works

Multidimensional Attention Convolution Plugin for Ship Radiated Noise Classification

Zheng, Z., & Liu, P. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-7188-5_20