Abstract
The research on multi-target recognition of mixed underwater acoustic signals is of great significance for military missions, ocean development, and navigation safety assurance. Due to the limited availability of information and the significant impact of the seawater medium and marine environmental noise on mono-channel underwater acoustic signals, achieving reliable and accurate multi-target recognition remains challenging. To overcome the challenge of reliability in multi-target recognition of mixed underwater acoustic signals, this paper focuses on investigating a deep learning-based method for recognizing and classifying underwater acoustic signals. A recurrent neural network fused with deep convolutional networks is proposed for multi-target recognition of mixed underwater acoustic signals. The deep convolutional network, DenseNet, is employed to extract frequency domain features, and an attention mechanism is introduced to capture the most salient features. Finally, hybrid underwater acoustic signal recognition is achieved through a recurrent neural network LSTM. The analysis of experimental results demonstrates that the DenseNet-LSTM model can enhance the accuracy of mixed underwater acoustic signal recognition based on frequency domain features. Furthermore, by incorporating the attention mechanism, the recognition rate is further improved.
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References
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)
**, 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)
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)
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
Hu, G., Wang, K.J., Liu, L.L.: Underwater acoustic target recognition based on depthwise separable convolution neural networks. Sensors (Basel) 4, 1429 (2021)
Sang, V.D., Huynh-The, T., Kim, D.S.: Underwater acoustic target classification based on dense convolutional neural network. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2020)
Liu, F., Shen, T., Luo, Z., Zhao, D., Guo, S.: Underwater target recognition using convolutional recurrent neural networks with 3-d mel-spectrogram and data augmentation. Appl. Acoust. 178, 107989 (2021)
Kumar, C.S.A., Maharana, A.D., et al.: Speech emotion recognition using CNN-LSTM and vision transformer. In: Innovations in Bio-Inspired Computing and Applications. IBICA 2022. Lecture Notes in Networks and Systems, vol. 649 (2022). https://doi.org/10.1007/978-3-031-27499-2_8
Rayan, A., Alaerjan, A.S., et al.: Utilizing CNN-LSTM techniques for the enhancement of medical systems. Alexandria Eng. J. 72, 323–338 (2023)
Rafi, S.H., Nahid-Al-Masood, S.R., Deeba, E.H.: A short-term load forecasting method using integrated CNN and LSTM network. IEEE Access 9, 32436–32448 (2021)
Xue, L., Zeng, X., **, A.: A novel deep-learning method with channel attention mechanism for underwater target recognition. Sensors 22(15), 5492 (2022)
**ao, X., Wang, W., Ren, Q., Gerstoft, P., et al.: Underwater acoustic target recognition using attention-based deep neural network. JASA Express Lett. 1(10), 106001-1–106001-8 (2021)
Yao, Y., Zeng, X., Wang, H., Liu, J.: Research on underwater acoustic target recognition method based on densenet. In: 2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), **’an, China, pp. 114–118 (2022)
Gao, Y., Chen,Y., Wang, F., et al.: Recognition method for underwater acoustic target based on DCGAN and DenseNet. In: 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC). Bei**g, China: IEEE, pp. 215–221 (2020)
Deng, Z., Jiang, Z., Lan, R., Huang, W., Luo, X.: Image captioning using DenseNet network and adaptive attention. Signal Process. Image Commun. 85, 1–9 (2020)
Tang, J., Li, Y., Ding, M., Liu, H., Yang, D., Wu, X.: An Ionospheric TEC forecasting model based on a CNN-LSTM-attention mechanism neural network. Remote Sens. 14(10), 2433 (2022)
Chen, C., Fan, L.: CNN-LSTM-ATTENTION deep learning model for map** landslide susceptibility in kerala, INDIA. ISPRS Ann. Photogrammetry Remote Sens. Spat. Inf. Sci. X-3/W1-2022, 25–30 (2022). https://doi.org/10.5194/isprs-annals-X-3-W1-2022-25-2022
Wang, J., Wang, R., Zeng, X.: Short-term passenger flow forecasting using ceemdan meshed Cnn-Lstm-attention model under wireless sensor network. IET Commun. 16, 1253–1263 (2022)
Chung, W.H., Gu, Y.H., Yoo, S.J.: District heater load forecasting based on machine learning and parallel CNN-LSTM attention. Energy 246, 123350 (2022)
Akmal, M.: Tensor factorization and attention-based CNN-LSTM deep-learning architecture for improved classification of missing physiological sensors data. IEEE Sens. J. 23(2), 1286–1294 (2023)
Santos-Domínguez, D., Torres-Guijarro, S., Cardenal-López, A.., Pena-Gimenez, A., et al.: ShipsEar: An underwater vessel noise database. Appl. Acoust. 113, 64–69 (2016)
Acknowledgments
This research received partial support from the R&D plan project in key fields of Guangdong Province (No. 2018B010109001, No. 2021B0707010001), as well as the key scientific research platform of universities in Guangdong Province (No. 2022KSYS016).
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Zhu, M., Zhang, X., Jiang, Y., Wang, K., Su, B., Wang, T. (2023). Hybrid Underwater Acoustic Signal Multi-Target Recognition Based on DenseNet-LSTM with Attention Mechanism. In: Deng, Z. (eds) Proceedings of 2023 Chinese Intelligent Automation Conference. CIAC 2023. Lecture Notes in Electrical Engineering, vol 1082. Springer, Singapore. https://doi.org/10.1007/978-981-99-6187-0_72
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