Paper Abstract and Keywords |
Presentation |
2020-03-02 13:00
[Poster Presentation]
High-precision modeling of distortion stomp box by deep learning using spectral features Kento Yoshimoto, Daichi Kitahara, Akira Hirabayashi (Ritsumeikan Univ.) EA2019-124 SIP2019-126 SP2019-73 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
We propose a method for modeling distortion stomp box with high accuracy using a deep neural network, WaveNet. The conventional method using the WaveNet adopted the error-to-signal ratio (ESR) defined in time domain as the loss function. Then, the high-frequency components were not sufficiently reproduced. To reproduce more accurate high-frequency components, we modify the loss function by adding the error of the spectral feature. We use a short-time Fourier transform and a mel frequency spectrogram as the spectral feature. Numerical experiments using an Ibanez SD9 show that the proposed method can generate modeling sounds with more accurate high-frequency components. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Distortion stomp box / black-box modeling / WaveNet / loss function / spectral features / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 440, SIP2019-126, pp. 135-140, March 2020. |
Paper # |
SIP2019-126 |
Date of Issue |
2020-02-24 (EA, SIP, SP) |
ISSN |
Print edition: ISSN 0913-5685 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) |
Download PDF |
EA2019-124 SIP2019-126 SP2019-73 |
Conference Information |
Committee |
SP EA SIP |
Conference Date |
2020-03-02 - 2020-03-03 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Okinawa Industry Support Center |
Topics (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
SIP |
Conference Code |
2020-03-SP-EA-SIP |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
High-precision modeling of distortion stomp box by deep learning using spectral features |
Sub Title (in English) |
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Keyword(1) |
Distortion stomp box |
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black-box modeling |
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WaveNet |
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loss function |
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spectral features |
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1st Author's Name |
Kento Yoshimoto |
1st Author's Affiliation |
Ritsumeikan University (Ritsumeikan Univ.) |
2nd Author's Name |
Daichi Kitahara |
2nd Author's Affiliation |
Ritsumeikan University (Ritsumeikan Univ.) |
3rd Author's Name |
Akira Hirabayashi |
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Ritsumeikan University (Ritsumeikan Univ.) |
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Speaker |
Author-1 |
Date Time |
2020-03-02 13:00:00 |
Presentation Time |
90 minutes |
Registration for |
SIP |
Paper # |
EA2019-124, SIP2019-126, SP2019-73 |
Volume (vol) |
vol.119 |
Number (no) |
no.439(EA), no.440(SIP), no.441(SP) |
Page |
pp.135-140 |
#Pages |
6 |
Date of Issue |
2020-02-24 (EA, SIP, SP) |
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