# AudioSet-R: A Refined AudioSet with Multi-Stage LLM Label Reannotation

arXiv: https://arxiv.org/abs/2508.15429 · HTML: https://mlsanity.com/paper/2508.15429/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [ReasonAudio: A Benchmark for Evaluating Reasoning Beyond Matching in Text-Audio Retrieval](https://mlsanity.com/paper/2605.03361/) (2026)

## Abstract (arXiv)

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.