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Paper Abstract and Keywords
Presentation 2014-05-24 11:30
[研究紹介] A spectrogram-patch-input DNN model for detection and classification of acoustic events robust to speech overlapping scenarios
Miquel Espi, Masakiyo Fujimoto, Yotaro Kubo, Tomohiro Nakatani (NTT) SP2014-17
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents an acoustic event detection and classification method that learns features from spectrogram patches (i.e. concatenation of a certain number of consecutive spectrum frames) in an unsupervised manner, and integrates effortlessly within the deep neural network framework. Most AED use-cases happen in scenarios where speech overlaps with acoustic events, and while derived features (e.g. MFCCs, Mel-filter-banks) have traditionally characterized well the spectrum of speech, they are too dense and centered on specific frequencies to be used with non-speech tasks. Results show that the proposed model based on spectrogram-patch out-performs those based on derived features, as well as previous AED works.
Keyword (in Japanese) (See Japanese page) 
(in English) acoustic event detection / deep neural network / spectrogram patch / source sparsity / / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 52, SP2014-17, pp. 171-176, May 2014.
Paper # SP2014-17 
Date of Issue 2014-05-17 (SP) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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)
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Conference Information
Committee SP IPSJ-MUS  
Conference Date 2014-05-24 - 2014-05-25 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2014-05-SP-MUS 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) [研究紹介] A spectrogram-patch-input DNN model for detection and classification of acoustic events robust to speech overlapping scenarios 
Sub Title (in English)  
Keyword(1) acoustic event detection  
Keyword(2) deep neural network  
Keyword(3) spectrogram patch  
Keyword(4) source sparsity  
1st Author's Name Miquel Espi  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Masakiyo Fujimoto  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Yotaro Kubo  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
4th Author's Name Tomohiro Nakatani  
4th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker Author-1 
Date Time 2014-05-24 11:30:00 
Presentation Time 240 minutes 
Registration for SP 
Paper # SP2014-17 
Volume (vol) vol.114 
Number (no) no.52 
Page pp.171-176 
Date of Issue 2014-05-17 (SP) 

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