使用由波动方程正则化的深度核学习进行声场估计
Sound Field Estimation Using Deep Kernel Learning Regularized by the Wave Equation
AI总结：
本文针对声场估计中时空核难以解析处理的问题，提出用深度核学习直接从数据学习核，并利用波动方程正则化提升泛化能力，数值仿真验证了其表示优势和性能改进。
摘要：
在这项工作中，我们为基于高斯过程（GP）回归的声场估计引入了一种时空核。值得注意的是，高斯过程具有一个吸引人的特性，即声场是测量值的线性函数，从而允许从分布式麦克风测量中高效地估计声场。然而，为了确保解析可处理性，大多数现有的声场估计核都是在频域中构建的，并且针对每个频率独立形成。为了解决时空核的解析不可处理性问题，我们在此提出通过深度核学习直接从数据中学习核。此外，为了提高深度核的泛化能力，我们提出了一种利用波动方程对学习过程进行正则化的方法。通过数值仿真展示了深度核的表示优势以及通过使用波动方程正则化所获得的改进泛化性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
