Acoustic fingerprints in nature: A self-supervised learning approach for ecosystem activity monitoring

Abstract

According to the World Health Organization, healthy communities rely on well-functioning ecosystems. Clean air, fresh water, and nutritious food are inextricably linked to ecosystem health. Changes in biological activity convey important information about ecosystem dynamics, and understanding such changes is crucial for the survival of our species. Scientific edge cyberinfrastructures collect distributed data and process it in situ, often using machine learning algorithms. Most current machine learning algorithms deployed on edge cyberinfrastructures, however, are trained on data that does not accurately represent the real stream of data collected at the edge. In this work we explore the applicability of two new self-supervised learning algorithms for characterizing an insufficiently curated, imbalanced, and unlabeled dataset collected by using a set of nine microphones at different locations at the Morton Arboretum, an internationally recognized tree-focused botanical garden and research center in Lisle, IL. Our implementations showed completely autonomous characterization capabilities, such as the separation of spectrograms by recording location, month, week, and hour of the day. The models also showed the ability to discriminate spectrograms by biological and atmospheric activity, including rain, insects, and bird activity, in a completely unsupervised fashion. We validated our findings using a supervised deep learning approach and with a dataset labeled by experts, confirming competitive performance in several features. Toward explainability of our self-supervised learning approach, we used acoustic indices and false color spectrograms, showing that the topology and orientation of the clouds of points in the output space over a 24-h period are strongly linked to the unfolding of biological activity. Our findings show that self-supervised learning has the potential to learn from and process data collected at the edge, characterizing it with minimal human intervention. We believe that further research is crucial to extending this approach for complete autonomous characterization of raw data collected on distributed sensors at the edge.