Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models
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| Опубликовано в:: | arXiv.org (Dec 13, 2024), p. n/a |
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| Главный автор: | |
| Другие авторы: | , |
| Опубликовано: |
Cornell University Library, arXiv.org
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| Предметы: | |
| Online-ссылка: | Citation/Abstract Full text outside of ProQuest |
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| Краткий обзор: | 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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| ISSN: | 2331-8422 |
| Источник: | Engineering Database |