Kernel ridge regression based sound field estimation using a rigid spherical microphone array

TL;DR

This paper introduces a novel kernel ridge regression method for sound field estimation that explicitly accounts for the boundary conditions of a rigid spherical scatterer, improving accuracy in realistic scenarios.

Contribution

It develops a new sound field representation within kernel ridge regression that incorporates boundary constraints of a rigid sphere, addressing limitations of previous open-sphere assumptions.

Findings

Method outperforms existing approaches in simulations

Effective in real-world experiments with spherical microphone array

Accurately models boundary conditions of rigid scatterers

Abstract

We propose a sound field estimation method based on kernel ridge regression using a rigid spherical microphone array. Kernel ridge regression with physically constrained kernel functions, and further with kernel functions adapted to observed sound fields, have proven to be powerful tools. However, such methods generally assume an open-sphere microphone array configuration, i.e., no scatterers exist within the observation or estimation region. Alternatively, some approaches assume the presence of scatterers and attempt to eliminate their influence through a least-squares formulation. Even then, these methods typically do not incorporate the boundary conditions of the scatterers, which are not presumed to be known. In contrast, we exploit the fact the scatterer here is a rigid sphere. Meaning, both the virtual scattering source locations and the boundary conditions are well-defined. Based on this, we formulate the scattered sound field within the kernel ridge regression framework and propose a novel sound field representation incorporating a boundary constraint. The effectiveness of the proposed method is demonstrated through numerical simulations and real-world experiments using a newly developed spherical microphone array.