Automated data curation for self-supervised learning in underwater acoustic analysis

TL;DR

This paper presents an automated data curation pipeline that combines acoustic recordings with AIS data to create balanced datasets, enabling effective self-supervised learning for underwater sound analysis and ecosystem monitoring.

Contribution

It introduces a fully automated method to curate diverse, balanced underwater acoustic datasets by integrating AIS data and hierarchical clustering, advancing self-supervised learning applications.

Findings

Curated a balanced dataset from raw PAM data and AIS integration.

Enabled development of self-supervised models for marine monitoring.

Facilitated tasks like marine mammal detection and pollution assessment.

Abstract

The sustainability of the ocean ecosystem is threatened by increased levels of sound pollution, making monitoring crucial to understand its variability and impact. Passive acoustic monitoring (PAM) systems collect a large amount of underwater sound recordings, but the large volume of data makes manual analysis impossible, creating the need for automation. Although machine learning offers a potential solution, most underwater acoustic recordings are unlabeled. Self-supervised learning models have demonstrated success in learning from large-scale unlabeled data in various domains like computer vision, Natural Language Processing, and audio. However, these models require large, diverse, and balanced datasets for training in order to generalize well. To address this, a fully automated self-supervised data curation pipeline is proposed to create a diverse and balanced dataset from raw PAM data. It integrates Automatic Identification System (AIS) data with recordings from various hydrophones in the U.S. waters. Using hierarchical k-means clustering, the raw audio data is sampled and then combined with AIS samples to create a balanced and diverse dataset. The resulting curated dataset enables the development of self-supervised learning models, facilitating various tasks such as monitoring marine mammals and assessing sound pollution.