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Paper Abstract and Keywords
Presentation 2017-12-01 14:20
[Invited Talk] Blind Audio Source Separation based on Independent Component Analysis
Shoji Makino (Univ. of Tsukuba) EA2017-78
Abstract (in Japanese) (See Japanese page) 
(in English) This talk describes a method for the blind source separation (BSS) of convolutive mixtures of audio signals, especially speech. A statistical and computational technique, called independent component analysis (ICA), is examined. We provide examples to show how ICA criteria change as the number of audio sources increases. By achieving nonlinear decorrelation, nonstationary decorrelation, or time-delayed decorrelation, we can find source signals only from observed mixed signals. We then discuss a frequency-domain approach where simple instantaneous ICA is employed in each frequency bin. Particular attention is paid to the physical interpretation of BSS from the acoustical signal processing point of view. Frequency-domain BSS is shown to be equivalent to two sets of frequency domain adaptive microphone arrays, i.e., adaptive beamformers (ABFs). Although BSS makes spatial notch to a jammer as well as ABF, BSS has a strong advantage over ABF. BSS can be regarded as an intelligent version of ABF in the sense that it can adapt without any information on the array manifold or the target direction, and sources can be simultaneously active in BSS.
Keyword (in Japanese) (See Japanese page) 
(in English) Blind source separation / independent component analysis / convolutive mixtures / adaptive beamformers / microphone array / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 328, EA2017-78, pp. 107-107, Nov. 2017.
Paper # EA2017-78 
Date of Issue 2017-11-23 (EA) 
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 EA2017-78

Conference Information
Committee EA ASJ-H  
Conference Date 2017-11-30 - 2017-12-01 
Place (in Japanese) (See Japanese page) 
Place (in English) University of Auckland (New Zealand) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Engineering/Electro Acoustics, Psychological and Physiological Acoustics, and Related Topics 
Paper Information
Registration To EA 
Conference Code 2017-11-EA-H 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Blind Audio Source Separation based on Independent Component Analysis 
Sub Title (in English)  
Keyword(1) Blind source separation  
Keyword(2) independent component analysis  
Keyword(3) convolutive mixtures  
Keyword(4) adaptive beamformers  
Keyword(5) microphone array  
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1st Author's Name Shoji Makino  
1st Author's Affiliation University of Tsukuba (Univ. of Tsukuba)
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Speaker Author-1 
Date Time 2017-12-01 14:20:00 
Presentation Time 40 minutes 
Registration for EA 
Paper # EA2017-78 
Volume (vol) vol.117 
Number (no) no.328 
Page p.107 
#Pages 1 
Date of Issue 2017-11-23 (EA) 

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