LongAudio-RAG: Event-Grounded Question Answering over Multi-Hour Long Audio
Abstract
Long-duration audio is increasingly common in industrial and consumer settings, yet reviewing multi-hour recordings is impractical, motivating systems that answer natural-language queries with precise temporal grounding and minimal hallucination. Existing audio-language models show promise, but long-audio question answering remains difficult due to context-length limits. We introduce LongAudio-RAG (LA-RAG), a hybrid framework that grounds Large Language Model (LLM) outputs in retrieved, timestamped acoustic event detections rather than raw audio. Multi-hour streams are converted into structured event records stored in an SQL database, and at inference time the system resolves natural-language time references, classifies intent, retrieves only the relevant events, and generates answers using this constrained evidence. To evaluate performance, we construct a synthetic long-audio benchmark by concatenating recordings with preserved timestamps and generating template-based question-answer pairs for detection, counting, and summarization tasks. Finally, we demonstrate the practicality of our approach by deploying it in a hybrid edge-cloud environment, where the audio grounding model runs on-device on IoT-class hardware while the LLM is hosted on a GPU-backed server. This architecture enables low-latency event extraction at the edge and high-quality language reasoning in the cloud. Experiments show that structured, event-level retrieval significantly improves accuracy compared to vanilla Retrieval-Augmented Generation (RAG) or text-to-SQL approaches.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents LongAudio-RAG, a novel framework for event-grounded question answering over multi-hour audio, significantly advancing the capabilities of audio processing systems. The detailed methodology and experimental validation underscore its potential impact in the field of machine learning, particularly in audio-language integration and real-time analytics.
Methodology
The methodology presented in the paper is robust and well-structured, introducing a hybrid framework that effectively combines audio grounding with large language models (LLMs) for long audio question answering. The use of SQL databases for structured event records and the detailed approach to temporal reference resolution and intent classification are commendable. The paper clearly outlines the steps taken to convert long audio into actionable data, which is a significant advancement in the field of audio processing and natural language understanding.
Results & evaluation
The experimental evaluation is thorough, utilizing a synthetic long-audio benchmark that allows for controlled testing of the proposed system against various baselines, including RAG and text-to-SQL approaches. The results demonstrate a clear improvement in accuracy and response quality, validating the effectiveness of the proposed method. The use of both automated and human evaluations adds credibility to the findings.
Reproducibility
The paper provides a detailed description of the implementation stack and methodologies used, which enhances reproducibility. However, the lack of a public repository or demo URL limits the ability for others to replicate the work fully. The modular service-oriented architecture described could facilitate reproducibility if made available.
Limitations
The paper acknowledges limitations related to the accuracy of the Audio Grounding Model (AGM), which may affect downstream reasoning. Additionally, the synthetic nature of the benchmark may not fully capture the complexities of real-world audio environments, potentially limiting the generalizability of the results.
Broader impact
The proposed system has significant potential applications in various domains, including industrial monitoring, smart home technologies, and security systems. By enabling precise question answering over long audio recordings, it could enhance user interaction with audio data and improve operational efficiencies in many sectors. The paper presents LongAudio-RAG, a novel framework for event-grounded question answering over multi-hour audio, significantly advancing the capabilities of audio processing systems. The detailed methodology and experimental validation underscore its potential impact in the field of machine learning, particularly in audio-language integration and real-time analytics.
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