Underwater Source Detection and Classification for Signal-based Surveillance: Audio Dataset Curation and Cross-Domain Evaluation

2026-06-27

AI Summary

This study addresses the challenges of insufficient training data and poor cross-domain generalization in underwater acoustic machine learning, primarily due to the scarcity of publicly available labeled datasets. To this end, the authors construct a new underwater audio dataset comprising over one thousand annotated recordings spanning eight categories of biological and mechanical sound sources. They propose a lightweight CNN architecture incorporating boundary-aware loss and feature alignment to effectively mitigate class imbalance and domain shift. Evaluated within a newly established cross-domain framework, the method achieves an in-domain accuracy of 96.35% and demonstrates a 42.60% improvement in zero-shot ship detection performance on the ShipsEar dataset. The work also releases the data curation pipeline and reproducible benchmarks to support future research in underwater acoustics.

Abstract

Machine learning for underwater acoustics is constrained by the scarcity of publicly available labeled datasets. In contrast to air-acoustic domains, where large benchmarks enable rapid model development, underwater datasets are typically small and limited in acoustic diversity, restricting robust model training and cross-domain generalization. To help address this gap, we introduce a curated underwater audio dataset derived from an open-source maritime sound archive. The dataset contains over one thousand labeled audio segments across eight biologically and mechanically relevant acoustic classes, providing an additional resource for training models in data-limited underwater environments. Additionally, we establish a lightweight Convolutional Neural Network (CNN) baseline and propose a margin-enhanced loss with feature alignment to mitigate class confusion arising from data imbalance, acoustic similarity, and cross-domain mismatch. While the baseline achieves 96.35% in-domain accuracy, evaluation on ShipsEar reveals substantial domain shift; the proposed feature alignment improve zero-shot ship detection by 42.60%, demonstrating stronger robustness under distribution mismatch. We further release a transparent curation pipeline and reproducible benchmark to support future research on imbalance mitigation, domain adaptation, and data-efficient underwater acoustic classification.

Problem

• underwater acoustics
• dataset scarcity
• cross-domain generalization
• labeled data
• acoustic classification

Innovation

• underwater acoustic dataset
• feature alignment
• margin-enhanced loss
• cross-domain generalization
• lightweight CNN