Fast Acoustic Scattering Using Convolutional Neural Networks

Ziqi Fan; Vibhav Vineet; Hannes Gamper; Nikunj Raghuvanshi

ICASSP 2020accepted0 citations

Fast Acoustic Scattering Using Convolutional Neural Networks

Ziqi Fan, Vibhav Vineet, Hannes Gamper, Nikunj Raghuvanshi

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.

BibTeX

@inproceedings{icassp2020_fastacousticscat,
  title = {Fast Acoustic Scattering Using Convolutional Neural Networks},
  author = {Ziqi Fan and Vibhav Vineet and Hannes Gamper and Nikunj Raghuvanshi},
  booktitle = {ICASSP 2020},
  year = {2020}
}