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

TL;DR

AnuraSet is a comprehensive, open-access dataset of neotropical anuran calls designed to facilitate machine learning research for species identification in passive acoustic monitoring, aiding conservation efforts amid climate change.

Contribution

This paper introduces AnuraSet, a large-scale, annotated dataset of neotropical anuran calls, along with baseline models and open resources for species identification research.

Findings

Dataset includes 27 hours of recordings from 42 species.

Open access to raw data, annotations, and baseline models.

Highlights challenges in anuran call identification for machine learning.

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.