Paper Abstract and Keywords |
Presentation |
2022-03-02 13:25
[Poster Presentation]
Sound Field Estimation from Small Number of Observations by Deep Learning with Difference-Approximation-Based Helmholtz-Equation Loss Function Kazuhide Shigemi, Shoichi Koyama, TomohikoNakamura, Hiroshi Saruwatari (UTokyo) EA2021-85 SIP2021-112 SP2021-70 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
We propose a single-frequency sound field estimation method from a small number of observations that uses a loss function based on the Helmholtz equation for training a convolutional neural network (CNN).Conventional CNN-based sound field estimation methods can enhance the estimation accuracy by using the measurements of the target sound environment. However, since they treat the sound field as a two-dimensional array, their estimated results may be physically infeasible, i.e., those results do not always satisfy the Helmholtz equation. To overcome this problem, we propose a loss function using the difference approximation of the Helmholtz equation, which enables us to encompass the physical constraint of the sound field in the CNN training. Results of numerical experiments show that the proposed method can estimate the sound fields less deviated from the Helmholtz equation while maintaining the accuracy of the sound field estimation. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Sound Field Reconstruction / Physics-Informed Neural Networks / Helmholtz Equation / Finite Difference Approximation / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 383, EA2021-85, pp. 132-139, March 2022. |
Paper # |
EA2021-85 |
Date of Issue |
2022-02-22 (EA, SIP, SP) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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EA2021-85 SIP2021-112 SP2021-70 |
Conference Information |
Committee |
EA SIP SP IPSJ-SLP |
Conference Date |
2022-03-01 - 2022-03-02 |
Place (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
EA |
Conference Code |
2022-03-EA-SIP-SP-SLP |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Sound Field Estimation from Small Number of Observations by Deep Learning with Difference-Approximation-Based Helmholtz-Equation Loss Function |
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Keyword(1) |
Sound Field Reconstruction |
Keyword(2) |
Physics-Informed Neural Networks |
Keyword(3) |
Helmholtz Equation |
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Finite Difference Approximation |
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1st Author's Name |
Kazuhide Shigemi |
1st Author's Affiliation |
The University of Tokyo (UTokyo) |
2nd Author's Name |
Shoichi Koyama |
2nd Author's Affiliation |
The University of Tokyo (UTokyo) |
3rd Author's Name |
TomohikoNakamura |
3rd Author's Affiliation |
The University of Tokyo (UTokyo) |
4th Author's Name |
Hiroshi Saruwatari |
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The University of Tokyo (UTokyo) |
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Speaker |
Author-1 |
Date Time |
2022-03-02 13:25:00 |
Presentation Time |
120 minutes |
Registration for |
EA |
Paper # |
EA2021-85, SIP2021-112, SP2021-70 |
Volume (vol) |
vol.121 |
Number (no) |
no.383(EA), no.384(SIP), no.385(SP) |
Page |
pp.132-139 |
#Pages |
8 |
Date of Issue |
2022-02-22 (EA, SIP, SP) |
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