Full-Stack Bioacoustics: Field Kit to AI to Action (Workshop report)

TL;DR

This workshop report discusses integrating open hardware, software, and AI techniques to advance bioacoustic monitoring for wildlife research, aiming to unify fragmented efforts and promote future development.

Contribution

It brings together diverse stakeholders to share skills and build a cohesive vision for bioacoustic AI development, bridging gaps between technology and ecological research.

Findings

Workshop facilitated knowledge sharing among bioacoustic researchers.

Identified key challenges and opportunities in open hardware and AI integration.

Outlined future directions for bioacoustic AI development.

Abstract

Acoustic data (sound recordings) are a vital source of evidence for detecting, counting, and distinguishing wildlife. This domain of "bioacoustics" has grown in the past decade due to the massive advances in signal processing and machine learning, recording devices, and the capacity of data processing and storage. Numerous research papers describe the use of Raspberry Pi or similar devices for acoustic monitoring, and other research papers describe automatic classification of animal sounds by machine learning. But for most ecologists, zoologists, conservationists, the pieces of the puzzle do not come together: the domain is fragmented. In this Lorentz workshop we bridge this gap by bringing together leading exponents of open hardware and open-source software for bioacoustic monitoring and machine learning, as well as ecologists and other field researchers. We share skills while also building a vision for the future development of "bioacoustic AI". This report contains an overview of the workshop aims and structure, as well as reports from the six groups.