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Paper Abstract and Keywords
Presentation 2021-06-19 13:00
Low Loss Machine Learning for Digital Modeling of Distortion Stomp Boxes.
Yuto Matsunaga, Naofumi Aoki, Yoshinori Dobashi (Hokkaido Univ.), Tetsuya Kojima (NITTC) SP2021-11
Abstract (in Japanese) (See Japanese page) 
(in English) Distortion stomp boxes are one of the acoustic devices used on electric guitars. This device has attracted the interest of many guitarists. With the development of digital signal processing technology, a variety of acoustic devices are modeled in digital signal processing. However, distortion stomp boxes are difficult to model due to their nonlinearity. Therefore, research to improve the accuracy of the distortion stomp boxes has been widely conducted. With the recent development of machine learning technology, machine learning is used for modeling of distortion stomp boxes. In this study, we propose a technique based on Long Short-Term Memory (LSTM). The learning model of the proposed technique is constructed using a structure based on the Wiener model. In this paper, after explaining the proposed technique, we compare the proposed technique with a conventional technique that also uses LSTM, and report the results.
Keyword (in Japanese) (See Japanese page) 
(in English) Distortion stomp boxes / VA Modeling / Machine Learning / LSTM / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 66, SP2021-11, pp. 46-50, June 2021.
Paper # SP2021-11 
Date of Issue 2021-06-11 (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 SP2021-11

Conference Information
Committee SP IPSJ-SLP IPSJ-MUS  
Conference Date 2021-06-18 - 2021-06-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) OTOGAKU Symposium 2021 
Paper Information
Registration To SP 
Conference Code 2021-06-SP-SLP-MUS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Low Loss Machine Learning for Digital Modeling of Distortion Stomp Boxes. 
Sub Title (in English)  
Keyword(1) Distortion stomp boxes  
Keyword(2) VA Modeling  
Keyword(3) Machine Learning  
Keyword(4) LSTM  
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1st Author's Name Yuto Matsunaga  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Naofumi Aoki  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Yoshinori Dobashi  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
4th Author's Name Tetsuya Kojima  
4th Author's Affiliation National Institute of Technology, Tokyo College (NITTC)
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Speaker
Date Time 2021-06-19 13:00:00 
Presentation Time 120 
Registration for SP 
Paper # IEICE-SP2021-11 
Volume (vol) IEICE-121 
Number (no) no.66 
Page pp.46-50 
#Pages IEICE-5 
Date of Issue IEICE-SP-2021-06-11 


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