PlanRAG-Audio: Planning and Retrieval Augmented Generation for Long-form Audio Understanding
Abstract
Long-form audio understanding poses significant challenges for large audio language models (LALMs) due to the extreme length of audio sequences and the need to reason over heterogeneous acoustic cues distributed over time, such as speech content, speaker identity, emotion, and sound events. To address these challenges, we propose \textbf{PlanRAG-Audio}, a planning-based retrieval-augmented generation framework for scalable long-form audio understanding. Rather than having audio LALMs process entire recordings directly, PlanRAG-Audio explicitly plans which modalities and temporal spans are required for a given query, and retrieves only query-relevant information from a structured text and audio database. This retrieval planning enables effective reasoning over complex, cross-domain audio queries while substantially reducing the input length passed to the large language models. Experiments across a wide range of speech/audio retrieval demonstrate that PlanRAG-Audio improves reasoning accuracy and stabilizes performance as audio duration increases by decoupling inference cost from raw audio length.
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 main contribution of this paper is the introduction of PlanRAG-Audio, a novel framework that enhances long-form audio understanding by employing a planning-based retrieval mechanism to optimize the processing of heterogeneous audio cues. This work represents a meaningful advancement in the field, combining innovative methodologies with practical applications that could reshape how audio data is analyzed and utilized.
Methodology
The proposed methodology of PlanRAG-Audio is innovative, leveraging a planning-based retrieval-augmented generation framework that effectively addresses the challenges of long-form audio understanding. The approach is well-structured, utilizing a deterministic planning stage to optimize the retrieval process, which is a significant advancement over traditional methods that often rely solely on ASR or text-centric models. The integration of multiple modalities and the focus on temporal structure in audio data are commendable, showcasing a thoughtful design that enhances reasoning capabilities across diverse audio tasks.
Results & evaluation
The experimental evaluation is robust, with a comprehensive setup that includes a variety of tasks ranging from basic audio understanding to advanced reasoning challenges. The use of real-world datasets and the careful construction of evaluation metrics provide a solid foundation for assessing the framework's performance. The results demonstrate clear improvements in reasoning accuracy and stability across varying audio durations, validating the effectiveness of the proposed approach.
Reproducibility
The paper emphasizes reproducibility by using publicly available datasets for evaluation and detailing the experimental setup, including the generation of queries and the metrics used for performance assessment. However, the lack of a publicly accessible code repository limits the ability for others to fully replicate the experiments.
Limitations
The paper acknowledges several limitations, including the dependency on pretrained perception modules, which may constrain the overall performance of the framework. Additionally, the preprocessing stage introduces computational overhead, which could impact real-time applications. The reliance on keyword-based retrieval may also limit the expressiveness of the retrieval mechanism.
Broader impact
The implications of this work are significant, as it addresses a critical gap in long-form audio understanding, which is increasingly relevant in applications such as automated transcription, audio summarization, and interactive voice assistants. The framework has the potential to enhance user experience in various domains, including education, media, and accessibility, by enabling more effective and nuanced interactions with audio content. The main contribution of this paper is the introduction of PlanRAG-Audio, a novel framework that enhances long-form audio understanding by employing a planning-based retrieval mechanism to optimize the processing of heterogeneous audio cues. This work represents a meaningful advancement in the field, combining innovative methodologies with practical applications that could reshape how audio data is analyzed and utilized.
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