@inproceedings{fan2020fast,
author = {Fan, Ziqi and Vineet, Vibhav and Gamper, Hannes and Raghuvanshi, Nikunj},
title = {Fast acoustic scattering using convolutional neural networks},
booktitle = {ICASSP 2020 - IEEE International Conference on Acoustics, Speech and Signal Processing},
year = {2020},
month = {May},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/fast-acoustic-scattering-using-convolutional-neural-networks/},
}