Résumé
Conservation of bird species requires detailed knowledge of their spatiotemporal occurrence and distribution patterns. Over the past decade, passive acoustic monitoring (PAM) has become an essential tool to collect data on birds on ecologically relevant scales. However, these PAM efforts generate extensive datasets, and their comprehensive analysis remains challenging. Improved and fully automated acoustic analysis frameworks are needed to advance the field of avian conservation. The 2021 BirdCLEF challenge focused on developing and assessing automated analysis frameworks for avian vocalizations in continuous soundscape data. The primary task of the challenge was to detect and identify all bird calls within the hidden test dataset. This paper describes how the various algorithms were evaluated and synthesizes the results and lessons learned.