Reconstruction of Sound Field through Diffusion Models
This addresses sound field reconstruction for AR/VR and sound control, but it is incremental as it applies an existing diffusion model method to a new domain.
The paper tackled the problem of reconstructing sound fields in rooms for applications like AR/VR by proposing a conditional Denoising Diffusion Probabilistic Model (SF-Diff) that uses limited measurements to generate fields at unknown locations, and it outperformed a state-of-the-art kernel interpolation baseline with accurate reconstructions.
Reconstructing the sound field in a room is an important task for several applications, such as sound control and augmented (AR) or virtual reality (VR). In this paper, we propose a data-driven generative model for reconstructing the magnitude of acoustic fields in rooms with a focus on the modal frequency range. We introduce, for the first time, the use of a conditional Denoising Diffusion Probabilistic Model (DDPM) trained in order to reconstruct the sound field (SF-Diff) over an extended domain. The architecture is devised in order to be conditioned on a set of limited available measurements at different frequencies and generate the sound field in target, unknown, locations. The results show that SF-Diff is able to provide accurate reconstructions, outperforming a state-of-the-art baseline based on kernel interpolation.