用结构化检索提升长音频问答准确率，支持低延迟查询。
Event-Grounded Question Answering over Long Audio via Structured Retrieval
- 将音频转为带时间戳的事件记录，构建事件数据库进行高效检索。
- 在24小时家庭与工业场景中实现低延迟问答，准确率显著提升。
- 适合需要精准定位音频事件的应用，如智能监控与语音分析。
针对多小时音频中的自然语言问题回答，需可靠事件识别、时间定位与高效检索。本文提出LA-RAG（Long Audio Retrieval-Augmented Generation）框架，将音频转化为带时间戳的事件记录，存入事件数据库，并通过意图感知检索与大语言模型生成回答。该框架支持两种部署模式：离线建索引模式，对长录音预先处理以实现低延迟查询；推理时建索引模式，对短片段进行条件化定位。我们在24小时家庭物联网（Home-IoT）与工业物联网（Industrial-IoT）基准数据集，以及基于真实录音的CASTELLA-QA上评估了LA-RAG。结果表明，经过建索引后，查询响应延迟低，问答效果良好。同时揭示当前大型音频-语言模型在事件检测与时间定位间存在显著差距，而显式的时间戳定位能持续提升时间推理能力。这些发现确立了结构化建模与检索作为生成式音频-语言模型在实际部署中不可或缺的补充。
原文摘要 · Abstract (English)
Answering natural-language questions over multi-hour audio requires reliable event recognition, temporal grounding, and efficient retrieval. We present LA-RAG (Long Audio Retrieval-Augmented Generation), a structured framework that converts audio into timestamped event records, stores them in an event database, and answers questions using intent-aware retrieval and LLM-based generation. LA-RAG supports two deployment settings: offline grounding mode, in which long recordings are pre-indexed for low-latency querying, and inference-time grounding mode, which performs query-conditioned grounding over shorter, open-ended clips. We evaluate LA-RAG on controlled 24-hour Home-IoT and Industrial-IoT benchmarks and on CASTELLA-QA, derived from real-world audio recordings. The results demonstrate effective long-audio question answering with low query-time latency after grounding and indexing. They also reveal a substantial gap between event detection and temporal localization in current large audio-language models, while showing that explicit timestamped grounding consistently improves temporal reasoning. These findings establish structured grounding and retrieval as a practical complement to generative audio-language models for deployment-oriented long-audio understanding.
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