  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.
