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arXiv · 2406.07914

Can Large Language Models Understand Spatial Audio?

Changli Tang·Wenyi Yu·Guangzhi Sun·Xianzhao Chen·Tian Tan·Wei Li·Jun Zhang·Lu Lu·Zejun Ma·Yuxuan Wang·Chao Zhang
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Abstract

This paper explores enabling large language models (LLMs) to understand spatial information from multichannel audio, a skill currently lacking in auditory LLMs. By leveraging LLMs' advanced cognitive and inferential abilities, the aim is to enhance understanding of 3D environments via audio. We study 3 spatial audio tasks: sound source localization (SSL), far-field speech recognition (FSR), and localisation-informed speech extraction (LSE), achieving notable progress in each task. For SSL, our approach achieves an MAE of $2.70^{\circ}$ on the Spatial LibriSpeech dataset, substantially surpassing the prior benchmark of about $6.60^{\circ}$. Moreover, our model can employ spatial cues to improve FSR accuracy and execute LSE by selectively attending to sounds originating from a specified direction via text prompts, even amidst overlapping speech. These findings highlight the potential of adapting LLMs to grasp physical audio concepts, paving the way for LLM-based agents in 3D environments.

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Changli Tang, Wenyi Yu, Guangzhi Sun, Xianzhao Chen, Tian Tan, Wei Li, Jun Zhang, Lu Lu, Zejun Ma, Yuxuan Wang, Chao Zhang. 2024-06-12. Can Large Language Models Understand Spatial Audio?. https://arxiv.org/abs/2406.07914

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