Sound Field Estimation Using Deep Kernel Learning Regularized by the Wave Equation

TL;DR

This paper introduces a novel deep kernel learning approach for sound field estimation that incorporates wave equation regularization, enabling efficient and generalizable spatio-temporal sound field modeling from microphone data.

Contribution

It proposes a data-driven deep kernel learning method for spatio-temporal sound field estimation, regularized by the wave equation to enhance generalization and analytical tractability.

Findings

Deep kernel learning improves sound field estimation accuracy.

Wave equation regularization enhances model generalization.

Numerical simulations demonstrate the effectiveness of the approach.

Abstract

In this work, we introduce a spatio-temporal kernel for Gaussian process (GP) regression-based sound field estimation. Notably, GPs have the attractive property that the sound field is a linear function of the measurements, allowing the field to be estimated efficiently from distributed microphone measurements. However, to ensure analytical tractability, most existing kernels for sound field estimation have been formulated in the frequency domain, formed independently for each frequency. To address the analytical intractability of spatio-temporal kernels, we here propose to instead learn the kernel directly from data by the means of deep kernel learning. Furthermore, to improve the generalization of the deep kernel, we propose a method for regularizing the learning process using the wave equation. The representational advantages of the deep kernel and the improved generalization obtained by using the wave equation regularization are illustrated using numerical simulations.