标题： FlexSED：面向开放词汇声音事件检测 显示英文标题

标题： FlexSED: Towards Open-Vocabulary Sound Event Detection

摘要： 尽管在大规模声音事件检测（SED）系统方面取得了进展，这些系统能够处理数百种声音类别，但现有的多类分类框架仍然存在根本性的限制。 它们无法处理自由文本声音查询，这使得交互更加灵活和用户友好，并且缺乏零样本能力，且few-shot适应性较差。 虽然已经探索了基于文本查询的分离方法，但它们主要集中在源分离上，对于需要精确时间定位和在大型多样声音词汇中高效检测的SED任务来说并不适合。 在本文中，我们提出了FlexSED，一个开放词汇的声音事件检测系统。 FlexSED基于预训练的音频SSL模型和CLAP文本编码器，引入了编码器-解码器结构和自适应融合策略，以实现从预训练权重的有效连续训练。 为了确保稳健的监督，它还使用大语言模型（LLMs）在训练期间协助事件查询选择，解决了缺失标签相关的问题。 结果表明，与普通SED模型相比，FlexSED在AudioSet-Strong上表现出更优的性能，同时展现出强大的零样本和few-shot能力。 我们发布了代码和预训练模型，以支持基于FlexSED的未来研究和应用。 显示英文摘要

摘要： Despite recent progress in large-scale sound event detection (SED) systems capable of handling hundreds of sound classes, existing multi-class classification frameworks remain fundamentally limited. They cannot process free-text sound queries, which enable more flexible and user-friendly interaction, and they lack zero-shot capabilities and offer poor few-shot adaptability. Although text-query-based separation methods have been explored, they primarily focus on source separation and are ill-suited for SED tasks that require precise temporal localization and efficient detection across large and diverse sound vocabularies. In this paper, we propose FlexSED, an open-vocabulary sound event detection system. FlexSED builds on a pretrained audio SSL model and the CLAP text encoder, introducing an encoder-decoder composition and an adaptive fusion strategy to enable effective continuous training from pretrained weights. To ensure robust supervision, it also employs large language models (LLMs) to assist in event query selection during training, addressing challenges related to missing labels. As a result, FlexSED achieves superior performance compared to vanilla SED models on AudioSet-Strong, while demonstrating strong zero-shot and few-shot capabilities. We release the code and pretrained models to support future research and applications based on FlexSED.