With the rapid advancement of technology, the recognition of underwater acoustic signals in complex environments has become increasingly crucial. Currently, mainstream underwater acoustic signal recognition relies primarily on time-frequency analysis to extract spectral features, finding widespread applications in the field. However, existing recognition methods heavily depend on expert systems, facing limitations such as restricted knowledge bases and challenges in handling complex relationships. These limitations stem from the complexity and maintenance difficulties associated with rules or inference engines. Recognizing the potential advantages of deep learning in handling intricate relationships, this paper proposes a method utilizing neural networks for underwater acoustic signal recognition. The proposed approach involves continual learning of features extracted from spectra for the classification of underwater acoustic signals. Deep learning models can automatically learn abstract features from data and continually adjust weights during training to enhance classification performance.

翻译：随着技术的快速发展，复杂环境下的水声信号识别日益重要。目前主流的水声信号识别方法主要依赖时频分析提取频谱特征，在该领域得到广泛应用。然而，现有识别方法高度依赖专家系统，面临知识库受限、复杂关系处理困难等局限。这些局限源于规则或推理引擎的复杂性与维护难度。鉴于深度学习在复杂关系处理方面的潜在优势，本文提出一种利用神经网络进行水声信号识别的方案。该方法通过对频谱提取特征进行持续学习，实现水声信号分类。深度学习模型能够自动从数据中学习抽象特征，并在训练过程中持续调整权重以提升分类性能。