@inproceedings{yao-etal-2026-physwave,
title = "{P}hys{W}ave: Physics-Guided Latent Diffusion Models for Controllable Spatial Audio Generation",
author = "Yao, Lingfeng and
Huang, Chenpei and
Yang, Xingke and
Geng, Ziye and
Luo, Changqing and
Wang, Hao and
Liu, Jiang and
Pan, Miao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.784/",
pages = "18227--18242",
ISBN = "979-8-89176-459-0",
abstract = "Text-to-spatial audio generation, such as text-to-First-Order Ambisonics (FOA), provides a convenient way to create spatial audio for billion-dollar gaming and film industries. However, existing text-to-FOA methods are largely data-driven and may produce audio that violates acoustic relations between source direction and distance. They also separate descriptive and parametric control, forcing users to trade usability for precision. In this paper, we present PhysWave, a physics-guided latent diffusion model for controllable text-to-FOA generation. PhysWave unifies natural-language and trajectory control through a shared waypoint-caption representation, and augments diffusion training with two differentiable acoustic priors: spherical-harmonic direction consistency and inverse-square distance consistency. To support dynamic spatial generation, we further construct a 300K-clip FOA dataset with diverse sound categories and source trajectories. Extensive results show that the proposed priors help PhysWave generate spatially consistent FOA audio while maintaining competitive audio quality. Further analyses show that these physics priors improve spatial consistency during training and can also be used as inference-time guidance for training-free spatial refinement."
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<abstract>Text-to-spatial audio generation, such as text-to-First-Order Ambisonics (FOA), provides a convenient way to create spatial audio for billion-dollar gaming and film industries. However, existing text-to-FOA methods are largely data-driven and may produce audio that violates acoustic relations between source direction and distance. They also separate descriptive and parametric control, forcing users to trade usability for precision. In this paper, we present PhysWave, a physics-guided latent diffusion model for controllable text-to-FOA generation. PhysWave unifies natural-language and trajectory control through a shared waypoint-caption representation, and augments diffusion training with two differentiable acoustic priors: spherical-harmonic direction consistency and inverse-square distance consistency. To support dynamic spatial generation, we further construct a 300K-clip FOA dataset with diverse sound categories and source trajectories. Extensive results show that the proposed priors help PhysWave generate spatially consistent FOA audio while maintaining competitive audio quality. Further analyses show that these physics priors improve spatial consistency during training and can also be used as inference-time guidance for training-free spatial refinement.</abstract>
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%0 Conference Proceedings
%T PhysWave: Physics-Guided Latent Diffusion Models for Controllable Spatial Audio Generation
%A Yao, Lingfeng
%A Huang, Chenpei
%A Yang, Xingke
%A Geng, Ziye
%A Luo, Changqing
%A Wang, Hao
%A Liu, Jiang
%A Pan, Miao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yao-etal-2026-physwave
%X Text-to-spatial audio generation, such as text-to-First-Order Ambisonics (FOA), provides a convenient way to create spatial audio for billion-dollar gaming and film industries. However, existing text-to-FOA methods are largely data-driven and may produce audio that violates acoustic relations between source direction and distance. They also separate descriptive and parametric control, forcing users to trade usability for precision. In this paper, we present PhysWave, a physics-guided latent diffusion model for controllable text-to-FOA generation. PhysWave unifies natural-language and trajectory control through a shared waypoint-caption representation, and augments diffusion training with two differentiable acoustic priors: spherical-harmonic direction consistency and inverse-square distance consistency. To support dynamic spatial generation, we further construct a 300K-clip FOA dataset with diverse sound categories and source trajectories. Extensive results show that the proposed priors help PhysWave generate spatially consistent FOA audio while maintaining competitive audio quality. Further analyses show that these physics priors improve spatial consistency during training and can also be used as inference-time guidance for training-free spatial refinement.
%U https://aclanthology.org/2026.emnlp-main.784/
%P 18227-18242
Markdown (Informal)
[PhysWave: Physics-Guided Latent Diffusion Models for Controllable Spatial Audio Generation](https://aclanthology.org/2026.emnlp-main.784/) (Yao et al., EMNLP 2026)
ACL
- Lingfeng Yao, Chenpei Huang, Xingke Yang, Ziye Geng, Changqing Luo, Hao Wang, Jiang Liu, and Miao Pan. 2026. PhysWave: Physics-Guided Latent Diffusion Models for Controllable Spatial Audio Generation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 18227–18242, Budapest, Hungary. Association for Computational Linguistics.