Reconstruction of Sound Field Through Diffusion Models
Federico Miotello, Luca Comanducci, Mirco Pezzoli, Alberto Bernardini, Fabio Antonacci, Augusto Sarti
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. We conduct a comparative analysis with two state-of-the-art baseline methods, one relying on kernel interpolation and the other on deep learning.
BibTeX
@inproceedings{icassp2024_reconstructionof,
title = {Reconstruction of Sound Field Through Diffusion Models},
author = {Federico Miotello and Luca Comanducci and Mirco Pezzoli and Alberto Bernardini and Fabio Antonacci and Augusto Sarti},
booktitle = {ICASSP 2024},
year = {2024}
}