# A strongly annotated passive acoustic dataset for tropical bird monitoring

Source: [A strongly annotated passive acoustic dataset for tropical bird monitoring](<https://www.sota2.com/research/paper/6a0e919ef984739ed6738bbc>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-23T05:00:11.413Z

Authors: Daniela Ruiz, Juan Sebasti\\'an Ulloa, Zhongqi Miao, Nicol\\'as Betancourt, Maria Paula Toro-G\\'omez, Andr\\'es Hern\\'andez, Bruno Demuro, Eliana Barona-Cort\\'es, Angela Mendoza-Henao, Andr\\'es Sierra-Ricaurte, Sebasti\\'an P\\'erez-Pe\\~na, Rahul Dodhia, Pablo Arbel\\'aez, Juan M. Lavista Ferres

Published: 2026-05-20T00:36:06.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2605.20578>)

[PDF](<https://arxiv.org/pdf/2605.20578.pdf>)