Redirecting to original paper in 30 seconds...
Click below to go immediately or wait for automatic redirect
📄 Abstract
Abstract: Achieving immersive auditory experiences in virtual environments requires
flexible sound modeling that supports dynamic source positions. In this paper,
we introduce a task called resounding, which aims to estimate room impulse
responses at arbitrary emitter location from a sparse set of measured emitter
positions, analogous to the relighting problem in vision. We leverage the
reciprocity property and introduce Versa, a physics-inspired approach to
facilitating acoustic field learning. Our method creates physically valid
samples with dense virtual emitter positions by exchanging emitter and listener
poses. We also identify challenges in deploying reciprocity due to
emitter/listener gain patterns and propose a self-supervised learning approach
to address them. Results show that Versa substantially improve the performance
of acoustic field learning on both simulated and real-world datasets across
different metrics. Perceptual user studies show that Versa can greatly improve
the immersive spatial sound experience. Code, dataset and demo videos are
available on the project website: https://waves.seas.upenn.edu/projects/versa.
Authors (3)
Zitong Lan
Yiduo Hao
Mingmin Zhao
Submitted
October 23, 2025
Key Contributions
Introduces 'resounding', the task of estimating room impulse responses at arbitrary emitter locations from sparse measurements, using the reciprocity property. Proposes 'Versa', a physics-inspired, self-supervised learning approach to facilitate acoustic field learning, addressing challenges with emitter/listener gain patterns.
Business Value
Enables more realistic and immersive audio experiences in virtual and augmented reality applications, enhancing user engagement and presence.