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Paper Abstract and Keywords
Presentation 2021-11-26 15:00
[Poster Presentation] A Study on Removal of Train Running Noise using U-Net for Spectrogram Images
Motoki Ichikawa, Shota Sano, Jian Lin, Yuusuke Kawakita, Tsuyoshi Miyazaki, Hiroshi Tanaka (KAIT) SRW2021-49 SeMI2021-48 CNR2021-23
Abstract (in Japanese) (See Japanese page) 
(in English) In this manuscript, we present the results of a study on the effect of speech denoising using spectrogram images obtained by short-time Fourier transform of sound. We converted the mixed sound data, which is the sound of running trains actually recorded and superimposed on the speech of a newspaper reading corpus, into images and removed the noise using U-Net, a deep neural network. In order to confirm the effectiveness of this method, we compared its performance with that of the conventional denoising method, spectral subtraction, in images, and confirmed that better performance could be obtained. In addition, we applied the trained model to the noise removal using the running sound which changed depending on the train section, and investigated the effect of the difference of the running sound on the removal performance.
Keyword (in Japanese) (See Japanese page) 
(in English) Spectrogram, / Noise Removal / U-Net / Train running sound / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 266, SeMI2021-48, pp. 61-65, Nov. 2021.
Paper # SeMI2021-48 
Date of Issue 2021-11-18 (SRW, SeMI, CNR) 
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 SRW2021-49 SeMI2021-48 CNR2021-23

Conference Information
Committee SRW SeMI CNR  
Conference Date 2021-11-25 - 2021-11-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Kikai-Shinko-Kaikan Bldg. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) IoT Workshop 
Paper Information
Registration To SeMI 
Conference Code 2021-11-SRW-SeMI-CNR 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Removal of Train Running Noise using U-Net for Spectrogram Images 
Sub Title (in English)  
Keyword(1) Spectrogram,  
Keyword(2) Noise Removal  
Keyword(3) U-Net  
Keyword(4) Train running sound  
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1st Author's Name Motoki Ichikawa  
1st Author's Affiliation Kanagawa Institute of Technology (KAIT)
2nd Author's Name Shota Sano  
2nd Author's Affiliation Kanagawa Institute of Technology (KAIT)
3rd Author's Name Jian Lin  
3rd Author's Affiliation Kanagawa Institute of Technology (KAIT)
4th Author's Name Yuusuke Kawakita  
4th Author's Affiliation Kanagawa Institute of Technology (KAIT)
5th Author's Name Tsuyoshi Miyazaki  
5th Author's Affiliation Kanagawa Institute of Technology (KAIT)
6th Author's Name Hiroshi Tanaka  
6th Author's Affiliation Kanagawa Institute of Technology (KAIT)
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Speaker Author-1 
Date Time 2021-11-26 15:00:00 
Presentation Time 120 minutes 
Registration for SeMI 
Paper # SRW2021-49, SeMI2021-48, CNR2021-23 
Volume (vol) vol.121 
Number (no) no.265(SRW), no.266(SeMI), no.267(CNR) 
Page pp.74-78(SRW), pp.61-65(SeMI), pp.51-55(CNR) 
#Pages 5 
Date of Issue 2021-11-18 (SRW, SeMI, CNR) 

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