Reconstruction of Sound Field through Diffusion Models

• URL: http://arxiv.org/abs/2312.08821v2
• Date: Wed, 21 Feb 2024 16:15:40 GMT
• Title: Reconstruction of Sound Field through Diffusion Models
• Authors: Federico Miotello, Luca Comanducci, Mirco Pezzoli, Alberto Bernardini, Fabio Antonacci and Augusto Sarti
• Abstract summary: 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) 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.
• Score: 15.192190218332843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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