Reconstruction of Sound Field through Diffusion Models

Federico Miotello, Luca Comanducci, Mirco Pezzoli, Alberto Bernardini, Fabio Antonacci, Augusto Sarti
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)
2023-12-14 00:00:00
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
Empowered by ChatGPT