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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.09708 |
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| _version_ | 1866914560410124288 |
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| author | Kim, Kirak Kim, Sungyoung |
| author_facet | Kim, Kirak Kim, Sungyoung |
| contents | Room Impulse Responses (RIRs) enable realistic acoustic simulation, with applications ranging from multimedia production to speech data augmentation. However, acquiring high-quality real-world RIRs is labor-intensive, and data scarcity remains a challenge for data-driven RIR generation approaches. In this paper, we propose a novel approach to RIR generation by adapting a pre-trained text-to-audio model, demonstrating for the first time that large-scale generative audio priors can be effectively leveraged for the task. To address the lack of text-RIR paired data, we utilize a labeling pipeline leveraging vision-language models to extract acoustic descriptions from existing image-RIR datasets. We introduce an in-context learning strategy to accommodate free-form user prompts during inference. Evaluations including subjective listening test demonstrate that our model generates plausible RIRs. Audio examples are available on our demo website. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_09708 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adapting a Text-to-Audio Model for Room Impulse Response Generation Kim, Kirak Kim, Sungyoung Audio and Speech Processing Room Impulse Responses (RIRs) enable realistic acoustic simulation, with applications ranging from multimedia production to speech data augmentation. However, acquiring high-quality real-world RIRs is labor-intensive, and data scarcity remains a challenge for data-driven RIR generation approaches. In this paper, we propose a novel approach to RIR generation by adapting a pre-trained text-to-audio model, demonstrating for the first time that large-scale generative audio priors can be effectively leveraged for the task. To address the lack of text-RIR paired data, we utilize a labeling pipeline leveraging vision-language models to extract acoustic descriptions from existing image-RIR datasets. We introduce an in-context learning strategy to accommodate free-form user prompts during inference. Evaluations including subjective listening test demonstrate that our model generates plausible RIRs. Audio examples are available on our demo website. |
| title | Adapting a Text-to-Audio Model for Room Impulse Response Generation |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2603.09708 |