用移动麦克风估计声场，提升灵活性并降低计算成本。
Sound field estimation with moving microphones using kernel ridge regression
- 基于核岭回归与傅里叶模型，融合先验知识正则化。
- 方向加权使估计精度提升，真实与仿真数据均验证有效。
- 引入随机傅里叶特征，大幅降低计算开销，适合实时应用。
与固定麦克风相比，使用移动麦克风进行声场估计可提高灵活性、缩短测量时间并减少设备限制。本文提出一种基于核岭回归（KRR）的移动麦克风声场估计方法，其模型基于离散傅里叶变换和Herglotz波函数构建的离散时间连续空间声场模型。该方法能像固定麦克风的核方法一样引入先验知识作为正则化项，这是移动麦克风场景下的新尝试。通过引入方向加权，显著提升了声场估计性能，在仿真与真实数据上均得到验证。由于移动麦克风声场估计计算成本高，本文进一步提出一种基于随机傅里叶特征（RFF）的近似KRR方法，以实现计算效率与精度的权衡：相比原KRR方法，计算成本显著降低，但估计精度略有下降。
原文摘要 · Abstract (English)
Sound field estimation with moving microphones can increase flexibility, decrease measurement time, and reduce equipment constraints compared to using stationary microphones. In this paper a sound field estimation method based on kernel ridge regression (KRR) is proposed for moving microphones. The proposed KRR method is constructed using a discrete time continuous space sound field model based on the discrete Fourier transform and the Herglotz wave function. The proposed method allows for the inclusion of prior knowledge as a regularization penalty, similar to kernel-based methods with stationary microphones, which is novel for moving microphones. Using a directional weighting for the proposed method, the sound field estimates are improved, which is demonstrated on both simulated and real data. Due to the high computational cost of sound field estimation with moving microphones, an approximate KRR method is proposed, using random Fourier features (RFF) to approximate the kernel. The RFF method is shown to decrease computational cost while obtaining less accurate estimates compared to KRR, providing a trade-off between cost and performance.
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