Resounding Acoustic Fields with Reciprocity
Zitong Lan, Yiduo Hao, Mingmin Zhao
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 impulse responses at arbitrary emitter locations from a sparse set of 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 in different metrics.A perceptual user study shows Versa can greatly improve the immersive spatial sound experience.We will release the code and dataset upon acceptance.