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

• URL: http://arxiv.org/abs/2307.06860v1
• Date: Tue, 11 Jul 2023 22:25:21 GMT
• Title: AnuraSet: A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring
• Authors: Juan Sebasti\'an Ca\~nas, Maria Paula Toro-G\'omez, Larissa Sayuri Moreira Sugai, Hern\'an Dar\'io Ben\'itez Restrepo, Jorge Rudas, Breyner Posso Bautista, Lu\'is Felipe Toledo, Simone Dena, Ad\~ao Henrique Rosa Domingos, Franco Leandro de Souza, Selvino Neckel-Oliveira, Anderson da Rosa, V\'itor Carvalho-Rocha, Jos\'e Vin\'icius Bernardy, Jos\'e Luiz Massao Moreira Sugai, Carolina Em\'ilia dos Santos, Rog\'erio Pereira Bastos, Diego Llusia, Juan Sebasti\'an Ulloa
• Abstract summary: This paper introduces a large-scale dataset of anuran calls recorded by passive acoustic monitoring (PAM) 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. We highlight the challenges of the dataset to encourage machine learning researchers to solve the problem of anuran call identification towards conservation policy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 the 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 can be found on our GitHub repository https://github.com/soundclim/anuraset.

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