  Deep learning (DL) models have emerged as a powerful tool in avian
bioacoustics to assess environmental health. To maximize the potential of
cost-effective and minimal-invasive passive acoustic monitoring (PAM), DL
models must analyze bird vocalizations across a wide range of species and
environmental conditions. However, data fragmentation challenges a
comprehensive evaluation of generalization performance. Therefore, we introduce
the BirdSet dataset, comprising approximately 520,000 global bird recordings
for training and over 400 hours of PAM recordings for testing. Our benchmark
offers baselines for several DL models to enhance comparability and consolidate
research across studies, along with code implementations that include
comprehensive training and evaluation protocols.
