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Interactive Machine Learning Solutions for Acoustic Monitoring of Animal Wildlife in Biosphere Reserves

Thiago Gouvea; Hannes Kath; Ilira Troshani; Bengt Lüers; Patrícia P. Serafini; Ivan B. Campos; André S. Afonso; Sérgio M. F. M. Leandro; Lourens Swanepoel; Nicholas Theron; Anthony M. Swemmer; Daniel Sonntag
In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. International Joint Conference on Artificial Intelligence (IJCAI-2023), located at IJCAI, August 19-25, Macao, Macao, International Joint Conferences on Artificial Intelligence, 2023.


Biodiversity loss is taking place at accelerated rates globally, and a business-as-usual trajectory will lead to missing internationally established conservation goals. Biosphere reserves are sites designed to be of global significance in terms of both the biodiversity within them and their potential for sustainable development, and are therefore ideal places for the development of local solutions to global challenges. While the protection of biodiversity is a primary goal of biosphere reserves, adequate information on the state and trends of biodiversity remains a critical gap for adaptive management in biosphere reserves. Passive acoustic monitoring (PAM) is an increasingly popular method for continued, reproducible, scalable, and cost-effective monitoring of animal wildlife. PAM adoption is on the rise, but its data management and analysis requirements pose a barrier for adoption for most agencies tasked with monitoring biodiversity. As an interdisciplinary team of machine learning scientists and ecologists experienced with PAM and working at biosphere reserves in marine and terrestrial ecosystems on three different continents, we report on the co-development of interactive machine learning tools for semi-automated assessment of animal wildlife.