Hybrid Underwater Acoustic Signal Multi-Target Recognition Based on DenseNet-LSTM with Attention Mechanism
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-6187-0_72
Reference21 articles.
1. Miao, Y., Zakharov, Y.V., Sun, H., Li, J., Wang, J.: Underwater acoustic signal classification based on sparse time–frequency representation and deep learning. IEEE J. Oceanic Eng. 46(3), 952–962 (2021)
2. Jin, G., Liu, F., Hao, W., Song, Q.: Deep learning- based framework for expansion, recognition and classification of underwater acoustic signal. J. Exp. Theor. Artif. Intell. 32(2), 205–218 (2020)
3. Doan, V.S., Huynh-The, T., Kim, D.S.: Underwater acoustic target classification based on dense convolutional neural network. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022)
4. Guo, T., et al.: Underwater target detection and localization with feature map and CNN-based classification. In: 2022 4th International Conference on Advances in Computer Technology, Information Science and Communications (CTISC), Suzhou, China, pp. 1–8 (2022). https://doi.org/10.1109/CTISC54888.2022.9849785
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1. Underwater acoustic target recognition based on sub-band concatenated Mel spectrogram and multidomain attention mechanism;Engineering Applications of Artificial Intelligence;2024-07
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