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

[2504.13102] A Multi-task Learning Balanced Attention Convolutional Neural Network Model for Few-shot Underwater Acoustic Target Recognition Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Sound arXiv:2504.13102 (cs) [Submitted on 17 Apr 2025] 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 View a PDF of the paper titled A Multi-task Learning Balanced Attention Convolutional Neural Network Model for Few-shot Underwater Acoustic Target Recognition, by Wei Huang and 5 other authors View PDF HTML (experimental) 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. Subjects: Sound (cs.SD) ; Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS) Cite as: arXiv:2504.13102 [cs.SD] (or arXiv:2504.13102v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2504.13102 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Expert Systems with Applications,2026 Related DOI : https://doi.org/10.1016/j.eswa.2026.131597 Focus to learn more DOI(s) linking to related resources Submission history From: Wei Huang [ view email ] [v1] Thu, 17 Apr 2025 17:11:32 UTC (1,546 KB) Full-text links: Access Paper: View a PDF of the paper titled A Multi-task Learning Balanced Attention Convolutional Neural Network Model for Few-shot Underwater Acoustic Target Recognition, by Wei Huang and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SD < prev | next > new | recent | 2025-04 Change to browse by: cs cs.AI eess eess.AS References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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