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Paper Abstract and Keywords
Presentation 2025-08-04 13:30
A Study of Supervised Basis Matrix in Semi-Supervised Nonnegative Matrix Factorization for Cicada Noise Reduction in Traffic Noise Measurement
Satoshi Takei, Kenji Muto (SIT) EA2025-25
Abstract (in Japanese) (See Japanese page) 
(in English) High-quality measurement is important for measuring traffic noise. In summer, the measuring traffic noise becomes more difficult due to the superimposition of disturbing sounds, such as cicada noise. In this study, we proposed a frequency scaling transform for semi-supervised nonnegative matrix factorization (SSNMF) that incorporates amplitude variation in the pre-learned basis matrix in order to separate the disturbing signals. The characteristic of the proposed method is a scale transformation of the pre-learned basis matrix in the frequency domain. The transformation corrects the frequency displacement between the pre-learned basis matrix of cicada noise and the true cicada noise spectrum. We compared the proposed method with the conventional method for separating cicada noise and traffic noise using computer simulation. The results showed that the proposed method achieved higher separation accuracy than the conventional method when the frequency characteristics of the pre-learned basis matrix were significantly different from those of the true cicada noise. In contrast, the proposed method achieved the same separation accuracy as the conventional method when the frequency characteristics of the pre-learned basis matrix were similar to those of the true cicada noise.
Keyword (in Japanese) (See Japanese page) 
(in English) Traffic noise measurement / source separation / Semi-supervised nonnegative matrix factorization / Cicada noise / Frequency scaling transformation / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 141, EA2025-25, pp. 7-12, Aug. 2025.
Paper # EA2025-25 
Date of Issue 2025-07-28 (EA) 
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 EA2025-25

Conference Information
Committee EA ASJ-H  
Conference Date 2025-08-04 - 2025-08-05 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To EA 
Conference Code 2025-08-EA-H 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study of Supervised Basis Matrix in Semi-Supervised Nonnegative Matrix Factorization for Cicada Noise Reduction in Traffic Noise Measurement 
Sub Title (in English)  
Keyword(1) Traffic noise measurement  
Keyword(2) source separation  
Keyword(3) Semi-supervised nonnegative matrix factorization  
Keyword(4) Cicada noise  
Keyword(5) Frequency scaling transformation  
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1st Author's Name Satoshi Takei  
1st Author's Affiliation Shibaura Institute of Technology (SIT)
2nd Author's Name Kenji Muto  
2nd Author's Affiliation Shibaura Institute of Technology (SIT)
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Speaker Author-1 
Date Time 2025-08-04 13:30:00 
Presentation Time 30 minutes 
Registration for EA 
Paper # EA2025-25 
Volume (vol) vol.125 
Number (no) no.141 
Page pp.7-12 
#Pages 6 
Date of Issue 2025-07-28 (EA) 

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