Acoustic Volume Rendering for Neural Impulse Response Fields

Abstract

Realistic audio synthesis that captures accurate acoustic phenomena is essential for creating immersive experiences in virtual and augmented reality. Synthesizing the sound received at any position relies on the estimation of impulse response (IR), which characterizes how sound propagates in one scene along different paths before arriving at the listener position. In this paper, we present Acoustic Volume Rendering (AVR), a novel approach that adapts volume rendering techniques to model acoustic impulse responses. While volume rendering has been successful in modeling radiance fields for images and neural scene representations, IRs present unique challenges as time-series signals. To address these challenges, we introduce frequency-domain volume rendering and use spherical integration to fit the IR measurements. Our method constructs an impulse response field that inherently encodes wave propagation principles and achieves state of-the-art performance in synthesizing impulse responses for novel poses. Experiments show that AVR surpasses current leading methods by a substantial margin. Additionally, we develop an acoustic simulation platform, AcoustiX, which provides more accurate and realistic IR simulations than existing simulators. Code for AVR and AcoustiX are available at https://zitonglan.github.io/avr.

Cite

Text

Lan et al. "Acoustic Volume Rendering for Neural Impulse Response Fields." Neural Information Processing Systems, 2024. doi:10.52202/079017-1417

Markdown

[Lan et al. "Acoustic Volume Rendering for Neural Impulse Response Fields." Neural Information Processing Systems, 2024.](https://mlanthology.org/neurips/2024/lan2024neurips-acoustic/) doi:10.52202/079017-1417

BibTeX

@inproceedings{lan2024neurips-acoustic,
  title     = {{Acoustic Volume Rendering for Neural Impulse Response Fields}},
  author    = {Lan, Zitong and Zheng, Chenhao and Zheng, Zhiwei and Zhao, Mingmin},
  booktitle = {Neural Information Processing Systems},
  year      = {2024},
  doi       = {10.52202/079017-1417},
  url       = {https://mlanthology.org/neurips/2024/lan2024neurips-acoustic/}
}