BirdCLEF 2023

Motivation

Birds are excellent indicators of biodiversity change since they are highly mobile and have diverse habitat requirements. Changes in species assemblage and the number of birds can thus indicate the success or failure of a restoration project. However, frequently conducting traditional observer-based bird biodiversity surveys over large areas is expensive and logistically challenging. In comparison, passive acoustic monitoring (PAM) combined with new analytical tools based on machine learning allows conservationists to sample much greater spatial scales with higher temporal resolution and explore the relationship between restoration interventions and biodiversity in depth.

For this competition, you'll use your machine-learning skills to identify Eastern African bird species by sound. Specifically, you'll develop computational solutions to process continuous audio data and recognize the species by their calls. The best entries will be able to train reliable classifiers with limited training data. If successful, you'll help advance ongoing efforts to protect avian biodiversity in Africa, including those led by the Kenyan conservation organization NATURAL STATE.

Context

NATURAL STATE is working in pilot areas around Northern Mount Kenya to test the effect of various management regimes and states of degradation on bird biodiversity in rangeland systems. By using the machine learning algorithms developed within the scope of this competition, NATURAL STATE will be able to demonstrate the efficacy of this approach in measuring the success of restoration projects and the cost-effectiveness of the method. In addition, the ability to cost-effectively monitor the impact of restoration efforts on biodiversity will allow NATURAL STATE to test and build some of the first biodiversity-focused financial mechanisms to channel much-needed investment into the restoration and protection of this landscape upon which so many people depend. These tools are necessary to scale this cost-effectively beyond the project area and achieve our vision of restoring and protecting the planet at scale.

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

Task description

The challenge will be held on Kaggle and the evaluation mode will resemble the 2022 test mode (i.e., hidden test data, code competition). The evaluation metric for this contest is padded cmAP. In order to support accepting predictions for species with zero true positive labels and to reduce the impact of species with very few positive labels, prior to scoring we pad each submission and the solution with five rows of true positives. This means that even a baseline submission will get a relatively strong score. Participants are tasked to recognize vocalizing birds in a given 5s-segment of audio across a variety of soundscape recordings. The train data contains 264 species from Kenya, Africa, and the test set consists of 191 10-minute soundscapes. Xeno-canto provided 16,900 audio recordings which can be used to train a classifier.

How to participate ?

BirdCLEF 2023 will be held on Kaggle. Join the competition here: https://www.kaggle.com/competitions/birdclef-2023

Prizes

1st Place - $ 15,000
2nd Place - $ 10,000
3rd Place - $ 8,000
4th Place - $ 7,000
5th Place - $ 5,000

Best working note award (optional):

Participants of this competition are encouraged to submit working notes to the LifeCLEF 2023 conference. As part of the conference, a best BirdCLEF working note competition will be held. The top two winners of the best working note award will receive $2,500 each. See the Evaluation page for judging criteria.

Image credits: Ian Davies / Photo of Little Bee eater (Macaulay Library ML33600851)