Fast acoustic scattering using convolutional neural networks - Fan, Ziqi; Vineet, Vibhav; Gamper, Hannes; Raghuvanshi, Nikunj (ICASSP 2020 - IEEE International Conference on Acoustics, Speech and Signal Processing, 2020)
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.