Gaussian processes for sound field reconstruction.

This study examines the use of Gaussian process (GP) regression for sound field reconstruction. GPs enable the reconstruction of a sound field from a limited set of observations based on the use of a covariance function (a kernel) that models the spatial correlation between points in the sound field. Significantly, the approach makes it possible to quantify the uncertainty on the reconstruction in a closed form.

A Bayesian spherical harmonics source radiation model for sound field control.

In sound field reproduction and sound field control systems, the acoustic transfer functions between a set of sources and an extended reproduction area need to be accurately estimated in order to achieve good performance. This implies that large amounts of measurements should be performed if the area is large compared to the wavelengths of interest. In this paper, a method for reconstructing these transfer functions in highly damped conditions is proposed by using only a small number of measurements in the reproduction area.