{"type":"ScholarlyArticle","title":"Interactive Machine Learning Solutions for Acoustic Monitoring of Animal Wildlife in Biosphere Reserves","authors":[{"name":"Thiago S. Gouvêa","slug":"thiago-s-gouvea","given":"Thiago","family":"S. Gouvêa","affiliation":"German Research Center for Artificial Intelligence (DFKI) , Oldenburg , Germany; Applied Artificial Intelligence , University of Oldenburg , Germany"},{"name":"Hannes Kath","slug":"hannes-kath","given":"Hannes","family":"Kath","affiliation":"German Research Center for Artificial Intelligence (DFKI) , Oldenburg , Germany; Applied Artificial Intelligence , University of Oldenburg , Germany"},{"name":"Ilira Troshani","slug":"ilira-troshani","given":"Ilira","family":"Troshani","affiliation":"German Research Center for Artificial Intelligence (DFKI) , Oldenburg , Germany; Applied Artificial Intelligence , University of Oldenburg , Germany"},{"name":"Bengt Lüers","slug":"bengt-luers","given":"Bengt","family":"Lüers","affiliation":"German Research Center for Artificial Intelligence (DFKI) , Oldenburg , Germany"},{"name":"Patricia P. Serafini","slug":"patricia-p-serafini","given":"Patricia P.","family":"Serafini","affiliation":"National Center for Wild Bird Conservation and Research (CEMAVE) , Chico Mendes Institute for Biodiversity Conservation (ICMBio) , Brazil"},{"name":"Ivan B. Campos","slug":"ivan-b-campos","given":"Ivan B.","family":"Campos","affiliation":"National Center for Wild Bird Conservation and Research (CEMAVE) , Chico Mendes Institute for Biodiversity Conservation (ICMBio) , Brazil; Departamento de Biologia Geral , Universidade Federal de Minas Gerais (UFMG) , Brazil"},{"name":"André S. Afonso","slug":"andre-s-afonso","given":"André S.","family":"Afonso","affiliation":"Marine and Environmental Research Centre (MARE) , Aquatic Research Network , Department of Life Sciences , University of Coimbra , Portugal"},{"name":"Sergio M. F. M. Leandro","slug":"sergio-m-f-m-leandro","given":"Sergio M. F. M.","family":"Leandro","affiliation":"School of Tourism and Maritime Technology , Polytechnic of Leiria , Peniche , Portugal"},{"name":"Lourens Swanepoel","slug":"lourens-swanepoel","given":"Lourens","family":"Swanepoel","affiliation":"Department of Biological Sciences , Faculty of Science, Engineering and Agriculture , University of Venda , South Africa"},{"name":"Nicholas Theron","slug":"nicholas-theron","given":"Nicholas","family":"Theron","affiliation":"Kruger to Canyons Biosphere Region NPC , South Africa"},{"name":"Anthony M. Swemmer","slug":"anthony-m-swemmer","given":"Anthony M.","family":"Swemmer","affiliation":"South African Environmental Observation Network (SAEON) , Kruger National Park , South Africa"},{"name":"Daniel Sonntag","slug":"daniel-sonntag","given":"Daniel","family":"Sonntag","affiliation":"German Research Center for Artificial Intelligence (DFKI) , Oldenburg , Germany; Applied Artificial Intelligence , University of Oldenburg , Germany"}],"abstract":"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.","doi":"10.24963/ijcai.2023/711","url":"https://ijcai.org/proceedings/2023/711","pdf":"https://ijcai.org/proceedings/2023/0711.pdf","markdown":"https://ijcai.org/proceedings/2023/711.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/711","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good - Projects","track":"AI for Good - Projects","number":711,"pages":{"first":6405,"last":6413},"keywords":["AI for Good - Projects: Machine Learning","AI for Good - Projects: Multidisciplinary Topics and Applications","AI for Good - Projects: Humans and AI","AI for Good - Projects: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Amershi et al., 2014] Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. 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