Fast acoustic scattering using convolutional neural networks
This addresses the need for faster acoustic simulation in interactive auralization and noise control applications, representing an incremental improvement using existing methods on new data.
The paper tackled the problem of expensive numerical simulation for acoustic scattering effects by training a convolutional neural network to predict spatial loudness distributions, achieving a root-mean-squared error of less than 1 dB and over 100x speedup compared to full wave simulation.
Diffracted scattering and occlusion are important acoustic effects in interactive auralization and noise control applications, typically requiring expensive numerical simulation. We propose training a convolutional neural network to map from a convex scatterer's cross-section to a 2D slice of the resulting spatial loudness distribution. We show that employing a full-resolution residual network for the resulting image-to-image regression problem yields spatially detailed loudness fields with a root-mean-squared error of less than 1 dB, at over 100x speedup compared to full wave simulation.