Fast acoustic scattering using convolutional neural networks

Autor:	Fan, Ziqi, Vineet, Vibhav, Gamper, Hannes, Raghuvanshi, Nikunj
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Machine Learning Computer Science - Sound Electrical Engineering and Systems Science - Image and Video Processing Electrical Engineering and Systems Science - Signal Processing
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
Externí odkaz:	http://arxiv.org/abs/1911.01802 Zobrazit plný text záznamu View this record from Arxiv