A Multi-task Learning Balanced Attention Convolutional Neural Network Model for Few-shot Underwater Acoustic Target Recognition

• URL: http://arxiv.org/abs/2504.13102v1
• Date: Thu, 17 Apr 2025 17:11:32 GMT
• Title: A Multi-task Learning Balanced Attention Convolutional Neural Network Model for Few-shot Underwater Acoustic Target Recognition
• Authors: Wei Huang, Shumeng Sun, Junpeng Lu, Zhenpeng Xu, Zhengyang Xiu, Hao Zhang,
• Abstract summary: We propose a multi-task balanced channel attention convolutional neural network (MT-BCA-CNN)<n>Experiments show MT-BCA-CNN achieves 97% classification accuracy and 95% $F1$-score in 27-class few-shot scenarios.<n>This work provides an efficient solution for few-shot underwater acoustic recognition, advancing research in marine bioacoustics and sonar signal processing.
• Score: 6.499457102377181
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Underwater acoustic target recognition (UATR) is of great significance for the protection of marine diversity and national defense security. The development of deep learning provides new opportunities for UATR, but faces challenges brought by the scarcity of reference samples and complex environmental interference. To address these issues, we proposes a multi-task balanced channel attention convolutional neural network (MT-BCA-CNN). The method integrates a channel attention mechanism with a multi-task learning strategy, constructing a shared feature extractor and multi-task classifiers to jointly optimize target classification and feature reconstruction tasks. The channel attention mechanism dynamically enhances discriminative acoustic features such as harmonic structures while suppressing noise. Experiments on the Watkins Marine Life Dataset demonstrate that MT-BCA-CNN achieves 97\% classification accuracy and 95\% $F1$-score in 27-class few-shot scenarios, significantly outperforming traditional CNN and ACNN models, as well as popular state-of-the-art UATR methods. Ablation studies confirm the synergistic benefits of multi-task learning and attention mechanisms, while a dynamic weighting adjustment strategy effectively balances task contributions. This work provides an efficient solution for few-shot underwater acoustic recognition, advancing research in marine bioacoustics and sonar signal processing.

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