Résumé
The BirdCLEF 2024 challenge focused on the acoustic identification of understudied bird species in the Western Ghats, a biodiversity hotspot in India. This edition aimed to advance passive acoustic monitoring by tasking participants with developing reliable systems for detecting and identifying bird vocalizations from extensive soundscape recordings. Using training data provided by the Xeno-Canto community and new unlabeled soundscapes from the Western Ghats, participants addressed the challenges of domain adaptation and limited training data for many species. Participants employed techniques such as pseudo-labeling, test-time augmentation, and diverse ensembles, significantly improving model performance. Notable strategies also included the use of single-class cross-entropy and Contrastive Adversarial Domain (CAD) bottlenecks, which provided innovative solutions to acoustic data analysis challenges. The highest-scoring submission achieved an ROC-AUC score of 0.690 on the private leaderboard (0.738 on the public leaderboard), with the top 10 systems differing by only 1.5% in their scores.