Recognizing underwater targets from acoustic signals is a challenging task owing to the intricate ocean environments and variable underwater channels. While deep learning-based systems have become the mainstream approach for underwater acoustic target recognition, they have faced criticism for their lack of interpretability and weak generalization performance in practical applications. In this work, we apply the class activation mapping (CAM) to generate visual explanations for the predictions of a spectrogram-based recognition system. CAM can help to understand the behavior of recognition models by highlighting the regions of the input features that contribute the most to the prediction. Our explorations reveal that recognition models tend to focus on the low-frequency line spectrum and high-frequency periodic modulation information of underwater signals. Based on the observation, we propose an interpretable contrastive learning (ICL) strategy that employs two encoders to learn from acoustic features with different emphases (line spectrum and modulation information). By imposing constraints between encoders, the proposed strategy can enhance the generalization performance of the recognition system. Our experiments demonstrate that the proposed contrastive learning approach can improve the recognition accuracy and bring significant improvements across various underwater databases.

翻译：由于复杂的海洋环境和多变的水下信道，从声学信号中识别水下目标是一项具有挑战性的任务。尽管基于深度学习的水下目标声学识别系统已成为主流方法，但其在实际应用中因缺乏可解释性和泛化性能薄弱而备受批评。本研究利用类激活映射（CAM）为基于频谱图的识别系统预测生成可视化解释。CAM通过突出对预测贡献最大的输入特征区域，有助于理解识别模型的行为机制。我们的研究发现，识别模型倾向于关注水下信号的低频线谱和高频周期性调制信息。基于此观察，我们提出一种可解释对比学习（ICL）策略，该策略采用双编码器从不同侧重点的声学特征（线谱与调制信息）中学习。通过在编码器间施加约束，所提策略可增强识别系统的泛化性能。实验表明，这种对比学习方法能够提升识别准确率，并在多个水下数据集上取得显著改进效果。