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. 2022 Apr 29;17(4):e0266425.
doi: 10.1371/journal.pone.0266425. eCollection 2022.

Underwater acoustic target recognition method based on a joint neural network

Xing Cheng Han  1   2 , Chenxi Ren  1   2 , Liming Wang  1   2 , Yunjiao Bai  3
Affiliations

Underwater acoustic target recognition method based on a joint neural network

Xing Cheng Han et al. PLoS One. .

Abstract

To improve the recognition accuracy of underwater acoustic targets by artificial neural network, this study presents a new recognition method that integrates a one-dimensional convolutional neural network and a long short-term memory network. This new network framework is constructed and applied to underwater acoustic target recognition for the first time. Ship acoustic data are used as input to evaluate the network performance. A visual analysis of the recognition results is performed. The results show that this method can realize the recognition and classification of underwater acoustic targets. Compared with a single neural network, the relevant indices, such as the recognition accuracy of the joint network are considerably higher. This provides a new direction for the application of deep learning in the field of underwater acoustic target recognition.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. 1D-Convolutional neural network model.
Fig 2
Fig 2. LSTM block model diagram.
Fig 3
Fig 3. Data processing flow chart.
Fig 4
Fig 4. t-SNE visualization result of the Mel spectrum, MFCCs feature and fusion feature.
(a) Mel spectrum; (b) MFCCs; (c) fusion feature.
Fig 5
Fig 5. Joint network model.
Fig 6
Fig 6. Variation of accuracy.
Fig 7
Fig 7. Variation of loss.
Fig 8
Fig 8. Confusion matrices for three networks.
(a) LTSM; (b) 1D-CNN; (c) Joint Network.
Fig 9
Fig 9. Precision about different categories.
Fig 10
Fig 10. Recall about different categories.
Fig 11
Fig 11. F1 Score about different categories.
Fig 12
Fig 12. Comparison results of the recognition accuracy of the three networks.
(a) Class A; (b) Class B; (c) Class C; (d) Class D; (e) Class E; (f) Overall recognition accuracy.
See this image and copyright information in PMC

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