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Paper Abstract and Keywords
Presentation 2022-03-01 14:20
Fast Distortion Pedal Modeling with Fine-Tuning
Haruki Shoji, Kento Yoshimoto, Daiki Saka, Hiroki Kuroda, Daichi Kitahara, Kenichiro Tanaka, Akira Hirabayashi (Ritsumeikan Univ.) EA2021-75 SIP2021-102 SP2021-60
Abstract (in Japanese) (See Japanese page) 
(in English) We propose a fast modeling method for distortion pedals based on deep learning. For modeling many times with different pedals and settings, it is desired to shorten the training time per one model, but simply reducing the amount of training data decreases the modeling accuracy. In this paper, when modeling a target distortion pedal from a small amount of data, we propose to apply fine-tuning where network parameters well-trained for another distortion pedal are used as initial values. Numerical experiments show that the proposed method trains the model of the target distortion pedal very quickly from a small amount of data while maintaining the modeling accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) Distortion Pedal / Deep Learning / WaveNet / Transfer Learning / Fine-Tuning / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 384, SIP2021-102, pp. 70-75, March 2022.
Paper # SIP2021-102 
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)
Download PDF EA2021-75 SIP2021-102 SP2021-60

Conference Information
Committee EA SIP SP IPSJ-SLP  
Conference Date 2022-03-01 - 2022-03-02 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIP 
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) Fast Distortion Pedal Modeling with Fine-Tuning 
Sub Title (in English)  
Keyword(1) Distortion Pedal  
Keyword(2) Deep Learning  
Keyword(3) WaveNet  
Keyword(4) Transfer Learning  
Keyword(5) Fine-Tuning  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Haruki Shoji  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Kento Yoshimoto  
2nd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
3rd Author's Name Daiki Saka  
3rd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
4th Author's Name Hiroki Kuroda  
4th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
5th Author's Name Daichi Kitahara  
5th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
6th Author's Name Kenichiro Tanaka  
6th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
7th Author's Name Akira Hirabayashi  
7th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
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Speaker Author-1 
Date Time 2022-03-01 14:20:00 
Presentation Time 25 minutes 
Registration for SIP 
Paper # EA2021-75, SIP2021-102, SP2021-60 
Volume (vol) vol.121 
Number (no) no.383(EA), no.384(SIP), no.385(SP) 
Page pp.70-75 
#Pages 6 
Date of Issue 2022-02-22 (EA, SIP, SP) 

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