Reconstruction of Sound Field through Diffusion Models

Author:

Federico Miotello, Luca Comanducci, Mirco Pezzoli, Alberto Bernardini, Fabio Antonacci, Augusto Sarti

Keyword:

Electrical Engineering and Systems Science, Audio and Speech Processing, Audio and Speech Processing (eess.AS), Machine Learning (cs.LG), Sound (cs.SD), Signal Processing (eess.SP)

journal:

date:

2023-12-14 00:00:00

Abstract

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.

PDF: Reconstruction of Sound Field through Diffusion Models.pdf