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

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 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/.

Automated detection of Bornean white-bearded gibbon (Hylobates albibarbis) vocalisations using an open-source framework for deep learning

“…In this study, we categorized selected calls according to their quality (cf. Cañas et al 2023), although we acknowledge that the distinction of "clear", "faint" and "very faint" greatcall selections was not based on p measurements. These categories cannot be translated directly to distance; however, in most cases, it is likely that "clear" calls were recorded from gibbons singing closer to the ARUs.…”