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 in this domain. 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 general model performance. To overcome these challenges, we present the BirdSet benchmark, a unified framework consolidating research efforts with a holistic approach for classifying bird vocalizations in avian bioacoustics. BirdSet harmonizes 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 establishing baseline results of current models, BirdSet aims to facilitate comparability, guide subsequent data collection, and increase accessibility for newcomers to avian bioacoustics.

翻译：深度学习模型已成为鸟类生物声学中诊断环境健康与生物多样性的强大工具。然而，研究中的不一致性给这一领域的发展带来了显著挑战。为了充分利用生物声学在经济高效被动声学监测场景中的潜力，可靠的深度学习模型需要灵活分析多种物种与环境中的鸟类叫声。数据碎片化与研究透明度不足阻碍了对模型整体性能的全面评估。为应对这些挑战，我们提出了BirdSet基准——一个采用整体方法统一研究框架、用于鸟类生物声学中鸟类发声分类的综合平台。BirdSet将开源鸟类录音整理成结构化数据集集合，通过统一方法深入理解模型性能并识别不同任务中的潜在不足。通过建立当前模型的基线结果，BirdSet旨在促进研究可比性、指导后续数据采集，并降低初入鸟类生物声学领域的研究门槛。