Sound Event Detection (SED) plays a vital role in audio understanding, with applications in surveillance, smart cities, healthcare, and multimedia indexing. However, conventional SED systems operate under a closed-world assumption, limiting their effectiveness in real-world environments where novel acoustic events frequently emerge. Inspired by the success of open-world learning in computer vision, we introduce the Open-World Sound Event Detection (OW-SED) paradigm, where models must detect known events, identify unseen ones, and incrementally learn from them. To tackle the unique challenges of OW-SED, such as overlapping and ambiguous events, we propose a 1D Deformable architecture that leverages deformable attention to adaptively focus on salient temporal regions. Furthermore, we design a novel Open-World Deformable Sound Event Detection Transformer (WOOT) framework incorporating feature disentanglement to separate class-specific and class-agnostic representations, together with a one-to-many matching strategy and a diversity loss to enhance representation diversity. Experimental results demonstrate that our method achieves marginally superior performance compared to existing leading techniques in closed-world settings and significantly improves over existing baselines in open-world scenarios.

翻译：声音事件检测（SED）在音频理解中扮演着重要角色，广泛应用于监控、智慧城市、医疗健康以及多媒体索引等领域。然而，传统SED系统基于封闭世界假设运行，这限制了其在真实环境中的有效性，因为真实环境中常出现新颖的声学事件。受计算机视觉中开放世界学习成功经验的启发，我们提出开放世界声音事件检测（OW-SED）范式，其中模型需检测已知事件、识别未见事件，并从中进行增量学习。为应对OW-SED中的独特挑战（如重叠和模糊事件），我们提出一种基于一维可变形架构的方法，利用可变形注意力机制自适应地聚焦于显著的时间区域。此外，我们设计了一种新颖的开放世界可变形声音事件检测Transformer（WOOT）框架，该框架引入特征解耦以分离类别特定与类别无关的表示，并结合一对多匹配策略与多样性损失来增强表示多样性。实验结果表明，我们的方法在封闭世界设置下比现有领先技术略具优势，并在开放世界场景中显著优于现有基线方法。