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A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring

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العنوان: A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring
المؤلفون: Cañas, Juan Sebastián, Toro-Gómez, María Paula, Sugai, Larissa Sayuri Moreira, Benítez Restrepo, Hernán Darío, Rudas, Jorge, Posso Bautista, Breyner, Toledo, Luís Felipe, Dena, Simone, Domingos, Adão Henrique Rosa, de Souza, Franco Leandro, Neckel-Oliveira, Selvino, da Rosa, Anderson, Carvalho-Rocha, Vítor, Bernardy, José Vinícius, Sugai, José Luiz Massao Moreira, dos Santos, Carolina Emília, Bastos, Rogério Pereira, Llusia Genique, Diego, Ulloa, Juan Sebastian
المساهمون: UAM. Departamento de Ecología
سنة النشر: 2024
المجموعة: Universidad Autónoma de Madrid (UAM): Biblos-e Archivo
مصطلحات موضوعية: Acoustics, animals, anura, benchmarking, ecosystem, vocalization, Medio Ambiente
الوصف: Global change is predicted to induce shifts in anuran acoustic behavior, which can be studied through passive acoustic monitoring (PAM). Understanding changes in calling behavior requires automatic identification of anuran species, which is challenging due to the particular characteristics of neotropical soundscapes. In this paper, we introduce a large-scale multi-species dataset of anuran amphibians calls recorded by PAM, that comprises 27 hours of expert annotations for 42 different species from two Brazilian biomes. We provide open access to the dataset, including the raw recordings, experimental setup code, and a benchmark with a baseline model of the fine-grained categorization problem. Additionally, we highlight the challenges of the dataset to encourage machine learning researchers to solve the problem of anuran call identification towards conservation policy. All our experiments and resources have been made available at https://soundclim.github.io/anuraweb/ ; The authors acknowledge financial support from the intergovernmental Group on Earth Observations (GEO) and Microsoft, under the GEO-Microsoft Planetary Computer Programme (October 2021); São Paulo Research Foundation (FAPESP #2016/25358–3; #2019/18335–5); the National Council for Scientific and Technological Development (CNPq #302834/2020–6; #312338/2021–0, #307599/2021–3); National Institutes for Science and Technology (INCT) in Ecology, Evolution, and Biodiversity Conservation, supported by MCTIC/CNpq (proc. 465610/2014–5), FAPEG (proc. 201810267000023); CNPQ/MCTI/CONFAP-FAPS/PELD No 21/2020 (FAPESC 2021TR386); Comunidad de Madrid (2020-T1/AMB-20636, Atracción de Talento Investigador, Spain) and research projects funded by the European Commission (EAVESTROP–661408, Global Marie S. Curie fellowship, program H2020, EU); and the Ministerio de Economía, Industria y Competitividad (CGL2017–88764-R, MINECO/AEI/FEDER, Spain). We also thank Tom Denton for machine learning evaluation suggestions, dataset revision, and comments on the manuscript
نوع الوثيقة: article in journal/newspaper
وصف الملف: application/pdf
اللغة: English
تدمد: 2052-4463
Relation: Scientific Data; https://doi.org/10.1038/s41597-023-02666-2; info:eu-repo/grantAgreement/EC/H2020/661408/EU//EAVESTROP; Gobierno de España. CGL2017–88764-R; Comunidad de Madrid. 2020-T1/AMB-20636; Scientific Data 10.1 (2023): 771; http://hdl.handle.net/10486/712941; 771-1; 771-12; 10
DOI: 10.1038/s41597-023-02666-2
الاتاحة: http://hdl.handle.net/10486/712941
https://doi.org/10.1038/s41597-023-02666-2
Rights: © The Author(s) 2023 ; http://creativecommons.org/licenses/by/4.0/ ; Reconocimiento ; openAccess
رقم الانضمام: edsbas.97AFC06F
قاعدة البيانات: BASE
الوصف
تدمد:20524463
DOI:10.1038/s41597-023-02666-2