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

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 neotro...

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Bibliographic Details
Published in:Scientific data 2023-11, Vol.10 (1), p.771-771, Article 771
Main Authors: 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, Diego, Ulloa, Juan Sebastián
Format: Article
Language:English
Subjects:
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Acoustic tracking
Acoustics
Annotations
Automation
Benchmarks
Biodiversity
Calling behavior
Collaboration
Data collection
Data Descriptor
Datasets
Ecological research
Humanities and Social Sciences
Identification
Introduced species
Machine learning
multidisciplinary
Reptiles & amphibians
Science
Science (multidisciplinary)
Soundscapes
Wildlife conservation
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Description
Summary: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/ .
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-023-02666-2