20 citations · 26 across the 10 of their papers we have counts for
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cs.SD2024
Can Large Language Models Understand Spatial Audio?
Changli Tang, Wenyi Yu, Guangzhi Sun +8
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'…
cs.SD2024
MINT: Boosting Audio-Language Model via Multi-Target Pre-Training and Instruction Tuning
Hang Zhao, Yifei Xin, Zhesong Yu +3
In the realm of audio-language pre-training (ALP), the challenge of achieving cross-modal alignment is significant. Moreover, the integration of audio inputs with diverse distribut…
cs.SD2023★ 20 cited
SALMONN: Towards Generic Hearing Abilities for Large Language Models
Changli Tang, Wenyi Yu, Guangzhi Sun +6
Hearing is arguably an essential ability of artificial intelligence (AI) agents in the physical world, which refers to the perception and understanding of general auditory informat…