# A strongly annotated passive acoustic dataset for tropical bird monitoring

arXiv: https://arxiv.org/abs/2605.20578 · HTML: https://mlsanity.com/paper/2605.20578/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 12 of 12)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Computational bioacoustics with deep learning: a review and roadmap](https://mlsanity.com/paper/2112.06725/) (2021)
- [Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge](https://mlsanity.com/paper/1807.05812/) (2018)
- [BEANS: The Benchmark of Animal Sounds](https://mlsanity.com/paper/2210.12300/) (2022)
- [BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics](https://mlsanity.com/paper/2403.10380/) (2024)
- [BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics](https://mlsanity.com/paper/2312.07439/) (2023)
- [NIPS4Bplus: a richly annotated birdsong audio dataset](https://mlsanity.com/paper/1811.02275/) (2018)
- [A decade of DCASE: Achievements, practices, evaluations and future challenges](https://mlsanity.com/paper/2410.04951/) (2024)
- [Balanced Deep CCA for Bird Vocalization Detection](https://mlsanity.com/paper/2211.09376/) (2022)
- [Automatic recognition of element classes and boundaries in the birdsong with variable sequences](https://mlsanity.com/paper/1601.06248/) (2016)
- [Western Mediterranean wetlands bird species classification: evaluating small-footprint deep learning approaches on a new annotated dataset](https://mlsanity.com/paper/2207.05393/) (2022)
- [DB3V: A Dialect Dominated Dataset of Bird Vocalisation for Cross-corpus Bird Species Recognition](https://mlsanity.com/paper/2406.08517/) (2024)

## Abstract (arXiv)

Passive acoustic monitoring enables continuous, non-invasive biodiversity assessment across diverse ecosystems. The scale of these datasets has driven the adoption of machine learning, with supervised approaches showing strong performance. However, supervised methods require time-resolved annotated datasets, which remain scarce, especially in complex tropical soundscapes. We present PteroSet, a curated dataset of strongly annotated Neotropical bird vocalizations recorded in Puerto Asis (Putumayo) and Pivijay (Magdalena), Colombia, between 2023 and 2025. The dataset comprises 563 recordings (73.62 h) and 15,372 time-frequency annotations, including 6,702 events identified to the species level across 168 species. We release the annotations in a COCO-inspired JSON schema that unifies audio files, taxonomic categories, and labels for machine learning workflows. Beyond providing annotated data, PteroSet serves as a realistic benchmark that highlights key characteristics of tropical soundscapes, including acoustic co-occurrence and domain shift across recording sites. We provide a deep learning baseline for binary bird detection, demonstrating PteroSet's usability and the challenges it presents.