用大模型重标注AudioSet，提升音频标签质量
AudioSet-R: A Refined AudioSet with Multi-Stage LLM Label Reannotation
- 分三阶段用多模态提示链重构标签，提升准确性
- 在4个主流模型上测试，分类性能显著提升
- 适合做音频分类、数据清洗的科研与工程人员
AudioSet 是音频研究领域广泛使用的基准数据集，显著推动了各类音频任务的发展。然而，标签准确性和完整性问题长期存在，成为下游应用性能的瓶颈。为解决此问题，我们提出一个三阶段重标注框架，利用通用音频-语言基础模型，系统性提升 AudioSet 的标签质量。该框架采用跨模态提示策略，借鉴提示链思想，按序执行音频理解、标签生成和语义对齐三个子任务。基于该框架，我们构建了高质量、结构化的 AudioSet-R 版本。在代表性音频分类模型（包括 AST、PANNs、SSAST 与 AudioMAE）上的大量实验表明，性能均实现显著提升，验证了该方法在增强标签可靠性方面的通用性与有效性。代码已公开：https://github.com/colaudiolab/AudioSet-R。
原文摘要 · Abstract (English)
AudioSet is a widely used benchmark in the audio research community and has significantly advanced various audio-related tasks. However, persistent issues with label accuracy and completeness remain critical bottlenecks that limit performance in downstream applications.To address the aforementioned challenges, we propose a three-stage reannotation framework that harnesses general-purpose audio-language foundation models to systematically improve the label quality of AudioSet. The framework employs a cross-modal prompting strategy, inspired by the concept of prompt chaining, wherein prompts are sequentially composed to execute subtasks (audio comprehension, label synthesis, and semantic alignment). Leveraging this framework, we construct a high-quality, structured relabeled version of AudioSet-R. Extensive experiments conducted on representative audio classification models--including AST, PANNs, SSAST, and AudioMAE--consistently demonstrate substantial performance improvements, thereby validating the generalizability and effectiveness of the proposed approach in enhancing label reliability.The code is publicly available at: https://github.com/colaudiolab/AudioSet-R.
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