Fast acoustic scattering using convolutional neural networks
Autor: | Fan, Ziqi, Vineet, Vibhav, Gamper, Hannes, Raghuvanshi, Nikunj |
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Rok vydání: | 2019 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | 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. Comment: Accepted by ICASSP 2020 |
Databáze: | arXiv |
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