Humboldt - AI4G Bioacoustics Dataset

Read full text
Authors: Ruiz Daniela, Juan Sebastián Ulloa, Zhongqi Miao, Nicolas Betancourt, Andrés Hernández, Bruno Demuro, Eliana Barona‐Cortés, María Paula Toro-Gómez, Angela M. Mendoza-Henao, Andrés Sierra-Ricaurte, Sebastian Camilo Pérez-Peña, Rahul Dodhia
+2 more Pablo Arbeláez, Juan M Lavista Ferres
Cited by: 0

Abstract

This release provides a curated passive acoustic monitoring dataset of Neotropical bird vocalizations recorded in Colombia (Puerto Asís, Putumayo; Pijavay, Magdalena) between 2023 and 2025. Audio was collected using autonomous AudioMoth recorders and stored in WAV format, distributed in audios.zip. The collection comprises 563 recordings totaling 73.62 hours at a 192 kHz sample rate. The dataset contains 15,372 curated bird-event annotations at the taxonomic-group level, of which 6,702 include species-level determinations using standardized species codes in the species.csv lookup table. Each recording is paired with expert-generated annotations created in Raven Pro (labels.zip) and subsequently harmonized into a COCO-inspired JSON schema for bioacoustics. These annotations are provided in two versions: annotations_identification.json, containing all annotations, and annotations_species.json, containing only species-level annotations. Annotations are provided as strong labels with explicit temporal boundaries (t_min, t_max) and spectral bounds (f_min, f_max), enabling time-frequency localization and event detection tasks. Deployment details are distributed in metadata.csv. Additionally, model weights are included in checkpoints.zip, enabling benchmarking of bioacoustic models on this dataset. All code used for data processing and technical validation is available at microsoft/PteroSet to ensure reproducibility.

Fields of Study & Subjects

Animal Vocal Communication and Behavior Music and Audio Processing Avian ecology and behavior

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
9 February 2026
Language
EN
Document type
Dataset