Fast acoustic scattering using convolutional neural networks

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.

Given input binary image (left) representing object cross section, our CNN produces output acoustic fields for octave frequency bands (right, bottom row) that match closely with reference wave simulation (right, top row)