Résumé
The BirdCLEF+ 2025 challenge focused on the simultaneous acoustic identification of birds, amphibians, mammals and insects in the Middle Magdalena Valley, a biodiversity hotspot in Colombia. This edition aimed to advance passive acoustic monitoring by tasking participants with developing reliable systems for detecting and identifying multi-taxonomic vocalizations from extensive soundscape recordings. Using training data provided by museum collections, citizen science projects and new unlabeled soundscapes, participants addressed the challenge of out-of-distribution generalization under field conditions and limited training data for many species. Participants used data augmentation, pseudo-labeling, and self-training to enhance model robustness and accuracy, often refining pseudo-labels iteratively. For improved scores and runtime efficiency, teams commonly employed Test-Time Augmentation, ensemble methods, and optimized inference with dominant Sound Event Detection and CNN-based models, frequently pretraining on external datasets. The highest-scoring submission achieved an ROC-AUC score of 0.930 on the private leaderboard (0.933 on the public leaderboard), with the top 10 systems differing by only 0.9% in their scores.