This paper introduces the Event-Shifted Acoustic Scene (ESAS) dataset, a novel benchmark for evaluating the robustness of Acoustic Scene Classification (ASC) systems against unknown sound events. Existing ASC datasets typically contain recordings of clean and consistent audio, while real-world environments often include diverse and unexpected sound events. To bridge this gap, ESAS simulates real-world acoustic variability by injecting foreground sound events into background scenes with the assistance of large language models. In this work, we present the construction methodology, dataset statistics, and evaluation protocols. Furthermore, a comprehensive evaluation of state-of-the-art ASC systems is conducted using the ESAS benchmark. Experimental results reveal that existing ASC models suffer significant performance degradation when facing the event-shift challenge. The introduction of the ESAS dataset aims to drive future research toward event-robust ASC.

翻译：本文介绍了事件偏移声学场景（ESAS）数据集，这是一个用于评估声学场景分类（ASC）系统对未知声音事件鲁棒性的新型基准测试。现有的ASC数据集通常包含干净一致的音频记录，而现实世界环境往往包含多样且不可预测的声音事件。为弥合这一差距，ESAS通过借助大语言模型将前景声音事件注入背景场景，模拟了真实世界的声学变异性。本研究阐述了该数据集的构建方法、统计特征及评估协议，并利用ESAS基准对现有最优ASC系统进行了全面评估。实验结果表明，面对事件偏移挑战，现有ASC模型性能显著下降。ESAS数据集的提出旨在推动未来研究向事件鲁棒的ASC方向发展。