Increasing levels of anthropogenic noise from ships contribute significantly to underwater sound pollution, posing risks to marine ecosystems. This makes monitoring crucial to understand and quantify the impact of the ship radiated noise. Passive Acoustic Monitoring (PAM) systems are widely deployed for this purpose, generating years of underwater recordings across diverse soundscapes. Manual analysis of such large-scale data is impractical, motivating the need for automated approaches based on machine learning. Recent advances in automatic Underwater Acoustic Target Recognition (UATR) have largely relied on supervised learning, which is constrained by the scarcity of labeled data. Transfer Learning (TL) offers a promising alternative to mitigate this limitation. In this work, we conduct the first empirical comparative study of transfer learning for UATR, evaluating multiple pretrained audio models originating from diverse audio domains. The pretrained model weights are frozen, and the resulting embeddings are analyzed through classification, clustering, and similarity-based evaluations. The analysis shows that the geometrical structure of the embedding space is largely dominated by recording-specific characteristics. However, a simple linear probe can effectively suppress this recording-specific information and isolate ship-type features from these embeddings. As a result, linear probing enables effective automatic UATR using pretrained audio models at low computational cost, significantly reducing the need for a large amounts of high-quality labeled ship recordings.

翻译：日益增加的船舶人为噪声显著加剧了水下声污染，对海洋生态系统构成威胁。这使得监测工作对于理解和量化船舶辐射噪声的影响至关重要。被动声学监测系统为此目的被广泛部署，在不同声景中积累了多年的水下录音数据。对此类大规模数据进行人工分析并不现实，因此迫切需要基于机器学习的自动化方法。近期水下声学目标识别领域的进展主要依赖于监督学习，但该方法受限于标注数据的稀缺。迁移学习为缓解这一限制提供了有前景的替代方案。本研究首次对UATR的迁移学习进行了实证比较研究，评估了源自不同音频领域的多种预训练音频模型。预训练模型权重被冻结，所得嵌入通过分类、聚类和相似性评估进行分析。分析表明，嵌入空间的几何结构主要受录音特异性特征主导。然而，简单的线性探测能有效抑制这种录音特异性信息，并从这些嵌入中分离出船舶类型特征。因此，线性探测能够以较低计算成本，利用预训练音频模型实现有效的水下声学目标识别，显著减少对大量高质量标注船舶录音数据的需求。