BirdCLEF+ 2025

Tentative Timeline

• 10 March 2025: Competition Start at Kaggle
• 5 June 2025: Competition Deadline
• 18 June 2025: Deadline for submission of working note papers by participants [CEUR-WS proceedings]
• 27 June 2025: Notification of acceptance of working note papers [CEUR-WS proceedings]
• 7 July 2025: Camera-ready deadline for working note papers.
• 9-12 Sept 2025: CLEF 2025 Madrid - Spain

Motivation

Mobile and habitat-diverse species serve as valuable indicators of biodiversity change, as shifts in their assemblages and population dynamics can signal the success or failure of ecological restoration efforts. However, conducting traditional observer-based biodiversity surveys across large areas is both costly and logistically demanding. In contrast, passive acoustic monitoring (PAM), combined with modern machine learning techniques, enables conservationists to sample across broader spatial scales with greater temporal resolution, providing deeper insights into the relationship between restoration interventions and biodiversity.

Task Description

For this competition, you'll apply your machine-learning expertise to identify under-studied species based on their acoustic signatures. Specifically, you'll develop computational methods to process continuous audio data and recognize species from different taxonomic groups by their sounds. The most effective solutions will demonstrate the ability to train reliable classifiers with limited labeled data. If successful, your work will contribute to ongoing efforts to enhance biodiversity monitoring, including research initiatives in the lowlands of the Magdalena Valley of Colombia.

The broader goals for this Kaggle competition include:
(1) Identify species of different taxonomic groups in the Middle Magdalena Valley of Colombia/El Silencio Natural Reserve in soundscape data.
(2) Train machine learning models with very limited amounts of training samples for rare and endangered species.
(3) Enhance machine learning models with unlabeled data for improving detection/classification.

Thanks to your innovations, it will be easier for researchers and conservation practitioners to understand restoration activities' effect trends accurately. As a result, they'll be able to evaluate threats and adjust their conservation actions regularly and more effectively.

Kaggle competition

As in previous years, BirdCLEF+ 2025 will be held at Kaggle as a so-called "code competition". We will award $50,000 in prize money to the best scoring submissions. More information on the competition and on how to participate can be found here: BirdCLEF competition website.

Publication Track

All registered participants are encouraged to submit a working-note paper to peer-reviewed LifeCLEF proceedings (CEUR-WS) after the competition ends.
This paper must provide sufficient information to reproduce the final submitted runs.

We will award the two best working notes with a prize of $2,500

The results of the campaign appear in the working notes proceedings published by CEUR Workshop Proceedings (CEUR-WS.org).
Selected contributions among the participants will be invited for publication in the Springer Lecture Notes in Computer Science (LNCS) the following year.

For detailed instructions, please refer to SUBMISSION INSTRUCTIONS.

A summary of the most important points:

• All participating teams with at least one graded submission, regardless of the score, should submit a CEUR working notes paper.
• Submission of reports is done through EasyChair – please make absolutely sure that the author (names and order), title, and affiliation information you provide in EasyChair match the submitted PDF exactly
• Deadline for the submission of initial CEUR-WS Working Notes Papers (for the peer-review process): 18 June 2025
• Deadline for the submission of Camera Ready CEUR-WS Working Notes Papers:: 7 July 2025
• Templates are available here
• Working Notes Papers should cite both the LifeCLEF 2025 overview paper as well as the PlantCLEF task overview paper, citation information will be added in the Citations section below as soon as the titles have been finalized.

Acknowledgements

The building of the El Silencio Dataset was supported by Earth Species Project, Experiment.com and Footprint Coalition under a Science Engine grant “AI for Interspecies Communication". Compiling these extensive datasets was a major undertaking, and we are very thankful to the many domain experts who helped to collect and manually annotate the data for this competition. Specifically, we would like to thank (institutions and individual contributors in alphabetic order):

Chemnitz University of Technology
Stefan Kahl, Mario Lasseck, and Maximilian Eibl

Colección de Sonidos Ambientales Mauricio Álvarez Rebolledo (IAvH-CSA)
Maria Paula Toro-Gómez

Fundación Biodiversa Colombia
Paula Caycedo, Pedro Jose Cardona, Noel Torres, Hugo Torres, Ramiro Torres, Felipe Aragón, Luis Osorio, and Santiago Rosado

Google Research
Tom Denton

iNaturalist
Grant van Horn

Instituto Humboldt
Juan Sebastián Ulloa and Susana Rodríguez Buriticá

K. Lisa Yang Center for Conservation Bioacoustics
Stefan Kahl and Holger Klinck

LifeCLEF
Alexis Joly and Henning Müller

Universidad de los Andes
Jose Luis Benavides-Lopez

University College London
Juan Sebastián Cañas and Kate Jones

University of Edinburgh
Oisin Mac Aodha

Xeno-canto
Willem-Pier Vellinga, Bob Planqué