Abstract
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.
Abstract (translated)
船舶产生的人为噪音水平上升，对水下声音污染的贡献显著增加，给海洋生态系统带来了风险。因此，监测船辐射噪声的影响变得至关重要。被动声学监测（PAM）系统被广泛用于这一目的，生成了跨越各种声音环境多年的大量水下录音数据。手动分析如此大规模的数据几乎是不可能的，这促使人们寻求基于机器学习的自动化方法的需求。最近，在自动水下声目标识别（UATR）方面取得的进展大多依赖于监督学习，而这种学习方式受限于标注数据的稀缺性。迁移学习（TL）为缓解这一限制提供了有希望的选择。 在本研究中，我们进行了首个关于转移学习用于UATR的经验比较分析，评估了来自不同音频领域的一系列预训练模型的效果。我们冻结了这些预训练模型的权重，并通过分类、聚类和基于相似性的评价方法来分析生成的嵌入（embeddings）。 分析结果显示，在嵌入空间中的几何结构主要由特定录音的特点所主导。然而，简单的线性探测可以有效抑制这一特点的信息并从这些嵌入中分离出船型特征。因此，通过使用预训练音频模型进行线性探测，能够以较低的计算成本实现有效的自动UATR，并显著减少了对高质量标注船舶录音的需求。 这种方法不仅提高了研究效率，还为解决数据标注问题提供了一种创新方案，促进了水下声学目标识别技术的发展。
URL
https://arxiv.org/abs/2601.08358
