Résumé
As the "extinction capital of the world", Hawai'i has lost 68% of its native bird species, the consequences of which can harm entire ecosystems. With physical monitoring difficult, scientists have turned to sound recordings, as this approach could provide a passive, low labor, and cost-effective strategy for monitoring endangered bird populations. Current methods for processing large bioacoustic datasets involve manual review of each recording. This requires specialized training and prohibitively large amounts of time. Recent advances in machine learning have made it possible to automatically identify bird songs for common species with ample training data. However, it remains challenging to develop such tools for rare and endangered species. The main goal of the 2022 edition of BirdCLEF was to advance automated detection of rare and endangered bird species that lack large amounts of training data. The competition challenged participants to develop reliable analysis frameworks to detect and identify the vocalizations of rare bird species in continuous Hawaiian soundscapes utilizing limited training data.