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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...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10628131/ https://www.ncbi.nlm.nih.gov/pubmed/37932332 http://dx.doi.org/10.1038/s41597-023-02666-2 |
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author | 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 |
author_facet | 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 |
author_sort | Cañas, Juan Sebastián |
collection | PubMed |
description | 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/. |
format | Online Article Text |
id | pubmed-10628131 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106281312023-11-08 A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring 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 Sci Data Data Descriptor 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/. Nature Publishing Group UK 2023-11-06 /pmc/articles/PMC10628131/ /pubmed/37932332 http://dx.doi.org/10.1038/s41597-023-02666-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor 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 A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring |
title | A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring |
title_full | A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring |
title_fullStr | A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring |
title_full_unstemmed | A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring |
title_short | A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring |
title_sort | dataset for benchmarking neotropical anuran calls identification in passive acoustic monitoring |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10628131/ https://www.ncbi.nlm.nih.gov/pubmed/37932332 http://dx.doi.org/10.1038/s41597-023-02666-2 |
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