Sound Field Estimation Based on Physics-Constrained Kernel Interpolation
Adapted to Environment
Abstract
This manuscript was submitted to IEEE/ACM Transactions on Audio, Speech
and Language Processing and is currently undergoing review. In this
work, we propose a fully adaptive kernel function for interior sound
field interpolation that considers both directed and residual sound
fields and always satisfies the Helmholtz equation. The method
accomplishes this by assigning each component a different kernel
function. The directed field represents sound field components of
intense directionality that are sparsely distributed, and is represented
by a superposition of kernels with strong directionality. The residual
field represents the lower amplitudes and has much less predictable
behavior, and thus was assigned a neural network weighted kernel
function. We compared the proposed kernel to competing kernel
formulations in numerical simulations and in real data experiments.