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Paper Abstract and Keywords
Presentation 2025-03-19 09:45
Active Acoustic Sensing for Predicting Semantic Segmentation Results
Taiki Mori, Junpei Honma, Shogo Yonezawa, Seiya Kodama, Go Irie (TUS) PRMU2024-53
Abstract (in Japanese) (See Japanese page) 
(in English) Semantic segmentation plays a crucial role in understanding scenes captured by cameras. However, it becomes infeasible in environments with poor visibility or where photography is restricted. An alternative approach is to infer semantic segmentation from sound captured by microphones. Prior studies have explored passive acoustic sensing, which relies on naturally occurring sounds (e.g., human voices) to estimate segmentation results. However, this method struggles to detect silent objects and inherently faces challenges in accurately estimating object shapes. In this study, we investigate active acoustic sensing, where controlled sound emissions enable object detection through echo analysis. This approach overcomes the limitations of passive sensing by capturing reflections from silent objects and providing richer shape-related cues. Experimental results demonstrate that our approach significantly improves segmentation accuracy compared to the passive counterpart. Furthermore, we show that accuracy can be further enhanced through a customized data augmentation technique.
Keyword (in Japanese) (See Japanese page) 
(in English) active acoustic sensing / data augmentation / semantic segmentation / / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 445, PRMU2024-53, pp. 41-46, March 2025.
Paper # PRMU2024-53 
Date of Issue 2025-03-11 (PRMU) 
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 PRMU2024-53

Conference Information
Committee PRMU IPSJ-CVIM IBISML  
Conference Date 2025-03-18 - 2025-03-19 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To PRMU 
Conference Code 2025-03-PRMU-CVIM-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Active Acoustic Sensing for Predicting Semantic Segmentation Results 
Sub Title (in English)  
Keyword(1) active acoustic sensing  
Keyword(2) data augmentation  
Keyword(3) semantic segmentation  
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1st Author's Name Taiki Mori  
1st Author's Affiliation Tokyo University of Science (TUS)
2nd Author's Name Junpei Honma  
2nd Author's Affiliation Tokyo University of Science (TUS)
3rd Author's Name Shogo Yonezawa  
3rd Author's Affiliation Tokyo University of Science (TUS)
4th Author's Name Seiya Kodama  
4th Author's Affiliation Tokyo University of Science (TUS)
5th Author's Name Go Irie  
5th Author's Affiliation Tokyo University of Science (TUS)
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Speaker Author-1 
Date Time 2025-03-19 09:45:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2024-53 
Volume (vol) vol.124 
Number (no) no.445 
Page pp.41-46 
#Pages 6 
Date of Issue 2025-03-11 (PRMU) 

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