Gaussian processes for sound field reconstruction

Diego Caviedes-Nozal*, Nicolai A.B. Riis, Franz M. Heuchel, Jonas Brunskog, Peter Gerstoft, Efren Fernandez-Grande

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


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.

Original languageEnglish
JournalJournal of the Acoustical Society of America
Issue number2
Pages (from-to)1107-1119
Publication statusPublished - 1 Feb 2021

Bibliographical note

Funding Information:
The authors would like to thank Manuel Hahmann for the fruitful discussions. This work is part of the MONICA project and has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 732350. It is partly supported by the VILLUM foundation (Grant No. 19179, “Large scale acoustic holography”).

Publisher Copyright:
© 2021 Acoustical Society of America.

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