Fast acoustic scattering using convolutional neural networks

Ziqi Fan; Vibhav Vineet; Hannes Gamper; Nikunj Raghuvanshi

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arXiv · 1911.01802

Fast acoustic scattering using convolutional neural networks

Ziqi Fan·Vibhav Vineet·Hannes Gamper·Nikunj Raghuvanshi

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Abstract

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.

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Ziqi Fan, Vibhav Vineet, Hannes Gamper, Nikunj Raghuvanshi. 2019-10-30. Fast acoustic scattering using convolutional neural networks. https://arxiv.org/abs/1911.01802

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