Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models

• URL: http://arxiv.org/abs/2409.06223v3
• Date: Fri, 13 Dec 2024 17:29:55 GMT
• Title: Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models
• Authors: Arvind Krishna Sridhar, Yinyi Guo, Erik Visser,
• Abstract summary: The Audio Question Answering (AQA) task includes audio event classification, audio captioning, and open-ended reasoning. LALMs excel in general audio understanding, but are limited in temporal reasoning. This paper addresses these challenges and limitations in audio temporal reasoning.
• Score: 0.9285295512807729
• License:
• Abstract: The Audio Question Answering (AQA) task includes audio event classification, audio captioning, and open-ended reasoning. Recently, AQA has garnered attention due to the advent of Large Audio Language Models (LALMs). Current literature focuses on constructing LALMs by integrating audio encoders with text-only Large Language Models (LLMs) through a projection module. While LALMs excel in general audio understanding, they are limited in temporal reasoning, which may hinder their commercial applications and on-device deployment. This paper addresses these challenges and limitations in audio temporal reasoning. First, we introduce a data augmentation technique for generating reliable audio temporal questions and answers using an LLM. Second, we perform a further fine-tuning of an existing baseline using curriculum learning strategy to specialize in temporal reasoning without compromising performance on fine-tuned tasks. We demonstrate the performance of our model using state-of-the-art LALMs on public audio benchmark datasets. Third, we implement our AQA model on-device locally and investigate its CPU inference for edge applications.

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