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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. In this study, the relation between reconstruction based on GPs and classical reconstruction methods based on linear regression is examined from an acoustical perspective. Several kernels are analyzed for their potential in sound field reconstruction, and a hierarchical Bayesian parameterization is introduced, which enables the construction of a plane wave kernel of variable sparsity. The performance of the kernels is numerically studied and compared to classical reconstruction methods based on linear regression. The results demonstrate the benefits of using GPs in sound field analysis. The hierarchical parameterization shows the overall best performance, adequately reconstructing fundamentally different sound fields. The approach appears to be particularly powerful when prior knowledge of the sound field would not be available. V

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

This work investigates how the sound field created by a sound reinforcement system can be controlled at low frequencies. An indoor control method is proposed which actively absorbs the sound incident on a reflecting boundary using an array of secondary sources. The sound field is separated into incident and reflected components by a microphone array close to the secondary sources, enabling the minimization of reflected components by means of optimal signals for the secondary sources. The method is purely feed-forward and assumes constant room conditions. Three different sound field separation techniques for the modeling of the reflections are investigated based on plane wave decomposition, equivalent sources, and the Spatial Fourier transform. Simulations and an experimental validation are presented, showing that the control method performs similarly well at enhancing low frequency responses with the three sound separation techniques. Resonances in the entire room are reduced, although the microphone array and secondary sources are confined to a small region close to the reflecting wall. Unlike previous control methods based on the creation of a plane wave sound field, the investigated method works in arbitrary room geometries and primary source positions.