BirdSet: A Multi-Task Benchmark for Classification in Computational Avian Bioacoustics

Deep learning (DL) models have emerged as a powerful tool in avian bioacoustics to diagnose environmental health and biodiversity. However, inconsistencies in research pose notable challenges hindering progress. Reliable DL models need to analyze bird calls flexibly across various species and environments to fully harness the potential of bioacoustics in a cost-effective passive acoustic monitoring scenario. Data fragmentation and opacity across studies complicate a comprehensive evaluation of model performance. To overcome these challenges, we present the BirdSet benchmark, a unified framework consolidating research efforts with a holistic approach for the classification of bird vocalizations in computational avian bioacoustics. BirdSet aggregates open-source bird recordings into a curated dataset collection. This unified approach provides an in-depth understanding of model performance and identifies potential shortcomings across different tasks. By providing baseline results of current models, we aim to facilitate comparability and ease accessibility for newcomers. Additionally, we release an open-source package \benchmark containing a comprehensive data pipeline that enables easy and fast model evaluation, available at https://github.com/DBD-research-group/BirdSet.