Experimental reconstruction of sound fields over large spatial domains

Abstract

This talk discusses sensing methods and reconstruction techniques for characterizing sound fields over large spatial domains. We focus specifically on reconstructing reverberant soundfields in rooms and enclosures. Two complementary approaches are presented in the talk: on the one hand, we present a method that leverages on modeling the spatio-temporal and statistical properties of enclosed sound fields via wave expansions, enabling to estimate the sound field over a large volume of space. On the other hand, we present a Physics Informed Neural Network, which learns the governing wave equation using a small set of measured data and enables to meaningfully interpolate and extrapolate the sound field to any position where no observations are available. Experiments in real rooms are presented, including an opera hall and an auditoria for oral communication. The experiments successfully demonstrate the volumetric acquisition and three-dimensional characterization of the sound fields. The presented techniques can be of relevance in applications pertaining to immersive and navigable audio, architectural acoustics and heritage preservation.