Remotely detecting and classifying underwater acoustic targets is critical for environmental monitoring and defence. However, the complexity of ship-radiated and environmental noise poses significant challenges for accurate signal processing. While recent advancements in machine learning have improved classification accuracy, limited dataset availability and a lack of standardised experimentation hinder generalisation and robustness. This paper introduces GSE ResNeXt, a deep learning architecture integrating learnable Gabor convolutional layers with a ResNeXt backbone enhanced by squeeze-and-excitation attention. The Gabor filters serve as two-dimensional adaptive band-pass filters, extending the feature channel representation. Its combination with channel attention improves training stability and convergence while enhancing the model's ability to extract discriminative features. The model is evaluated using three training-test split strategies that reflect increasingly complex classification tasks, demonstrating how systematic evaluation design addresses issues such as data leakage, temporal separation, and taxonomy. Results show that GSE ResNeXt consistently outperforms baseline models like Xception, ResNet, and MobileNetV2, in terms of classification performance. Regarding stability and convergence, adding Gabor convolutions to the initial layers of the model reduced training time by up to 62%. During the evaluation of training-testing splits, temporal separation between subsets significantly affected performance, proving more influential than training data volume. These findings suggest that signal processing can enhance model reliability and generalisation under varying environmental conditions, particularly in data-limited underwater acoustic classification. Future developments should focus on mitigating environmental effects on input signals.

翻译：水下声学目标的远程探测与分类对于环境监测与国防应用至关重要。然而，船舶辐射噪声与环境噪声的复杂性给精确信号处理带来了重大挑战。尽管机器学习的最新进展提升了分类准确率，但有限的数据集可用性以及缺乏标准化实验阻碍了模型的泛化能力与鲁棒性。本文提出GSE ResNeXt深度学习架构，该架构将可学习的Gabor卷积层与通过压缩-激励注意力增强的ResNeXt主干网络相结合。Gabor滤波器作为二维自适应带通滤波器，扩展了特征通道的表征能力。其与通道注意力机制的融合提升了训练稳定性与收敛速度，同时增强了模型提取判别性特征的能力。本研究采用三种反映渐增复杂度的分类任务的训练-测试划分策略对模型进行评估，展示了系统化评估设计如何应对数据泄露、时序分离及分类体系等问题。结果表明，在分类性能方面，GSE ResNeXt持续优于Xception、ResNet和MobileNetV2等基线模型。在稳定性与收敛性方面，在模型初始层添加Gabor卷积使训练时间最高减少62%。在训练-测试划分评估中，子集间的时序分离显著影响模型性能，其影响力甚至超过训练数据量。这些发现表明，信号处理方法能够提升模型在不同环境条件下的可靠性与泛化能力，尤其在数据受限的水声分类任务中。未来研究应着重于缓解环境因素对输入信号的影响。