arXiv:2602.14612v4 Announce Type: replace-cross Abstract: Answering natural-language questions over multi-hour audio requires both event recognition and temporal grounding. Current large audio-language models perform well on short clips, but are limited by context length, query-time cost, and weak temporal localization. We present LA-RAG (Long Audio-Retrieval Augmented Generation), a structured framework that converts continuous audio into timestamped event records using an open-vocabulary Audio Grounding Model (AGM), stores them in a SQL event database, and answers queries through intent-awar
The proliferation of long-form audio content and the increasing sophistication of Large Audio-Language Models necessitates better methods for efficient and accurate content analysis.
This development allows for more effective information extraction from extensive audio, enabling new applications in analytics, intelligence, and accessibility, moving beyond current limitations of audio-language models.
The ability to process and query multi-hour audio with high temporal precision, previously a significant challenge, is now significantly improved, shifting the landscape for audio-based AI applications.
- · AI developers
- · Intelligence agencies
- · Content creators
- · Researchers
- · Manual audio transcription services
- · Legacy audio processing software
Improved efficiency and accuracy in analyzing large volumes of audio data.
Expansion of AI applications into domains heavily reliant on long-form audio, such as legal discovery, market research, and security.
The creation of new industries focused on structured audio data platforms and advanced audio-AI analytics.
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Read at arXiv cs.AI