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

arXiv: https://arxiv.org/abs/2307.06860 · HTML: https://mlsanity.com/paper/2307.06860/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 65 references that are in the map.
- First posted on arXiv 11 Jul 2023.

## Builds on (most cited first, 27 of 27)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Recognition in Terra Incognita](https://mlsanity.com/paper/1807.04975/) (2018)
- [Long-tail learning via logit adjustment](https://mlsanity.com/paper/2007.07314/) (2020)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning](https://mlsanity.com/paper/1806.06193/) (2018)
- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [Resnet in Resnet: Generalizing Residual Architectures](https://mlsanity.com/paper/1603.08029/) (2016)
- [Learning to Navigate for Fine-grained Classification](https://mlsanity.com/paper/1809.00287/) (2018)
- [The Devil is in the Tails: Fine-grained Classification in the Wild](https://mlsanity.com/paper/1709.01450/) (2017)
- [The Benefit Of Temporally-Strong Labels In Audio Event Classification](https://mlsanity.com/paper/2105.07031/) (2021)
- [TorchAudio: Building Blocks for Audio and Speech Processing](https://mlsanity.com/paper/2110.15018/) (2021)
- [Computational bioacoustics with deep learning: a review and roadmap](https://mlsanity.com/paper/2112.06725/) (2021)
- [AVES: Animal Vocalization Encoder based on Self-Supervision](https://mlsanity.com/paper/2210.14493/) (2022)
- [BEANS: The Benchmark of Animal Sounds](https://mlsanity.com/paper/2210.12300/) (2022)
- [A Network Perspective on Stratification of Multi-Label Data](https://mlsanity.com/paper/1704.08756/) (2017)
- [Improving Bird Classification with Unsupervised Sound Separation](https://mlsanity.com/paper/2110.03209/) (2021)
- [The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting](https://mlsanity.com/paper/2207.09295/) (2022)
- [Bugs in the Data: How ImageNet Misrepresents Biodiversity](https://mlsanity.com/paper/2208.11695/) (2022)
- [We don't need no bounding-boxes: Training object class detectors using only human verification](https://mlsanity.com/paper/1602.08405/) (2016)
- [HumBugDB: A Large-scale Acoustic Mosquito Dataset](https://mlsanity.com/paper/2110.07607/) (2021)
- [Learning to detect an animal sound from five examples](https://mlsanity.com/paper/2305.13210/) (2023)
- [Few-shot bioacoustic event detection at the DCASE 2022 challenge](https://mlsanity.com/paper/2207.07911/) (2022)
- [Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset](https://mlsanity.com/paper/2207.10664/) (2022)
- [Unsupervised classification to improve the quality of a bird song recording dataset](https://mlsanity.com/paper/2302.07560/) (2023)

## Built on by (most cited first, 10 of 10)

- [The iNaturalist Sounds Dataset](https://mlsanity.com/paper/2506.00343/) (2025)
- [Foundation Models for Bioacoustics -- a Comparative Review](https://mlsanity.com/paper/2508.01277/) (2025)
- [Clustering and novel class recognition: evaluating bioacoustic deep learning feature extractors](https://mlsanity.com/paper/2504.06710/) (2025)
- [Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds](https://mlsanity.com/paper/2403.09598/) (2024)
- [Robust detection of overlapping bioacoustic sound events](https://mlsanity.com/paper/2503.02389/) (2025)
- [Synthetic data enables context-aware bioacoustic sound event detection](https://mlsanity.com/paper/2503.00296/) (2025)
- [bacpipe: a Python package to make bioacoustic deep learning models accessible](https://mlsanity.com/paper/2604.11560/) (2026)
- [Inteligencia Artificial para la conservaci'on y uso sostenible de la biodiversidad, una visi'on desde Colombia (Artificial Intelligence for conservation and sustainable use of biodiversity, a view from Colombia)](https://mlsanity.com/paper/2503.14543/) (2025)
- [Ecologically-Constrained Task Arithmetic for Multi-Taxa Bioacoustic Classifiers Without Shared Data](https://mlsanity.com/paper/2605.03914/) (2026)
- [Active Learning for Biodiversity Monitoring: From Label Efficiency to Reliable Ecological Inference](https://mlsanity.com/paper/2609.27409/) (2026)

## Abstract (arXiv)

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.