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 manuscrip
