用刚性球麦克风阵列提升声场估计精度，突破传统方法对散射体的假设限制。
Kernel ridge regression based sound field estimation using a rigid spherical microphone array
- 基于核岭回归框架，引入刚性球边界约束建模散射声场。
- 在数值模拟与真实实验中均实现更优声场重建效果。
- 适合需要高精度声场建模的室内声学、音频工程场景。
本文提出一种基于核岭回归的声场估计方法，采用刚性球形麦克风阵列。现有方法通常假设麦克风阵列为开放球形（无散射体），或虽考虑散射体但未引入其边界条件。本研究利用刚性球散射体的明确边界特性，将虚拟散射源位置与边界条件纳入核岭回归框架，构建含边界约束的新声场表示。通过新研制的球形麦克风阵列进行数值仿真与真实实验验证，结果表明该方法在声场估计中具有显著优势。
原文摘要 · Abstract (English)
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.
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