2 research outputs found
Physics-informed convolutional neural network with bicubic spline interpolation for sound field estimation
A sound field estimation method based on a physics-informed convolutional
neural network (PICNN) using spline interpolation is proposed. Most of the
sound field estimation methods are based on wavefunction expansion, making the
estimated function satisfy the Helmholtz equation. However, these methods rely
only on physical properties; thus, they suffer from a significant deterioration
of accuracy when the number of measurements is small. Recent learning-based
methods based on neural networks have advantages in estimating from sparse
measurements when training data are available. However, since physical
properties are not taken into consideration, the estimated function can be a
physically infeasible solution. We propose the application of PICNN to the
sound field estimation problem by using a loss function that penalizes
deviation from the Helmholtz equation. Since the output of CNN is a spatially
discretized pressure distribution, it is difficult to directly evaluate the
Helmholtz-equation loss function. Therefore, we incorporate bicubic spline
interpolation in the PICNN framework. Experimental results indicated that
accurate and physically feasible estimation from sparse measurements can be
achieved with the proposed method.Comment: Accepted to International Workshop on Acoustic Signal Enhancement
(IWAENC) 202