In this section
@article{BORREL-JENSEN:2024:10.3397/in_2024_4238,
author = {BORREL-JENSEN, N and MEYER-KAHLEN, N},
doi = {10.3397/in_2024_4238},
journal = {INTER-NOISE and NOISE-CON Congress and Conference Proceedings},
pages = {9369--9377},
title = {Synthesizing source directivity using DeepONet room acoustic predictions},
url = {http://dx.doi.org/10.3397/in_2024_4238},
volume = {270},
year = {2024}
}
TY - JOUR
AB - <jats:p>Incorporating source directivity when simulating spatial room impulse responses for virtual environments is crucial for achieving natural results. Directly injecting the directional source as the initial condition for a wave equation to solve would require re-running a numerical solver for each new directivity pattern, source position or orientation. Here we show that learned surrogate models can lead to large efficiency benefits if such variation shall be simulated. Although training such models is a computationally expensive process, performing inference over them is typically fast. Here we show how to exploit that property by training a deep neural operator network (DeepONet) only using omni-directional sources, and predicting the pressure on a variable grid of points, to which directivity filters are applied to generate arbitrary patterns. We show the fit of directictivity patterns to their re-synthesized versions from the output of a DeepONet.</jats:p>
AU - BORREL-JENSEN,N
AU - MEYER-KAHLEN,N
DO - 10.3397/in_2024_4238
EP - 9377
PY - 2024///
SN - 0736-2935
SP - 9369
TI - Synthesizing source directivity using DeepONet room acoustic predictions
T2 - INTER-NOISE and NOISE-CON Congress and Conference Proceedings
UR - http://dx.doi.org/10.3397/in_2024_4238
UR - https://doi.org/10.3397/in_2024_4238
VL - 270
ER -
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