Decodable but not structured: linear probing enables Underwater Acoustic Target Recognition with pretrained audio embeddings

TL;DR

This paper demonstrates that linear probing of pretrained audio embeddings can effectively perform underwater acoustic target recognition, reducing the need for extensive labeled data and computational resources.

Contribution

It is the first empirical study showing that simple linear probes can isolate ship-type features from pretrained audio embeddings for underwater acoustic recognition.

Findings

Linear probing suppresses recording-specific information in embeddings.

Pretrained models enable effective UATR with minimal labeled data.

Embedding space is dominated by recording characteristics, but can be disentangled.

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.