Resounding Acoustic Fields with Reciprocity

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.

In summary, our work makes the following contributions: • We leverage reciprocity in wave propagation and propose Versa-ELE, a simple yet effective strategy to augment sparse emitter data by generating physically valid virtual samples.

• We introduce Versa-SSL, a self-supervised learning framework that enforces reciprocity-based consistency in model predictions and generalizes to asymmetric directional gain patterns. These methods serve as a general machine learning training strategy grounded in physical reciprocity.

• We implement Versa on multiple models with comprehensive evaluations. Results demonstrate Versa significantly improves acoustic field estimation and enables perceptually realistic resounding.