Audiopedia: Audio QA with Knowledge

2024-12-29

AI Summary

This work addresses the challenge of integrating multi-segment audio information with external knowledge in knowledge-intensive Audio Question Answering (Audio QA). We formally introduce “Knowledge-Augmented Audio QA” as a novel task, comprising three subtasks: single-audio understanding, cross-sample multi-audio reasoning, and retrieval-augmented generation. To tackle this, we propose KA²LM—a general-purpose knowledge-augmented framework that unifies Audio Entity Linking (AEL) and Retrieval-Augmented Generation (RAG), enabling arbitrary audio large language models to perform knowledge-aware reasoning. Experiments demonstrate substantial performance gains on complex Audio QA benchmarks, particularly in scenarios requiring domain-specific background knowledge and joint interpretation of heterogeneous audio sources. Our approach establishes a knowledge-driven paradigm for audio understanding, advancing audio foundation models toward deeper semantic comprehension and contextual reasoning.

Technology Category

• Knowledge Representation and Reasoning: Knowledge Acquisition
• Natural Language Processing: Question Answering
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

In this paper, we introduce Audiopedia, a novel task called Audio Question Answering with Knowledge, which requires both audio comprehension and external knowledge reasoning. Unlike traditional Audio Question Answering (AQA) benchmarks that focus on simple queries answerable from audio alone, Audiopedia targets knowledge-intensive questions. We define three sub-tasks: (i) Single Audio Question Answering (s-AQA), where questions are answered based on a single audio sample, (ii) Multi-Audio Question Answering (m-AQA), which requires reasoning over multiple audio samples, and (iii) Retrieval-Augmented Audio Question Answering (r-AQA), which involves retrieving relevant audio to answer the question. We benchmark large audio language models (LALMs) on these sub-tasks and observe suboptimal performance. To address this, we propose a generic framework that can be adapted to any LALM, equipping them with knowledge reasoning capabilities. Our framework has two components: (i) Audio Entity Linking (AEL) and (ii) Knowledge-Augmented Audio Large Multimodal Model (KA2LM), which together improve performance on knowledge-intensive AQA tasks. To our knowledge, this is the first work to address advanced audio understanding via knowledge-intensive tasks like Audiopedia.

Problem

• Audio Understanding
• Machine Listening
• Complex Question Answering

Innovation

• Audiopedia
• Two-Step Strategy
• Audio Understanding Enhancement