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| Presentation | 2019-03-14 15:15 Convergence-guaranteed independent positive semidefinite tensor analysis for blind source separation Kanta Fukushige, Norihiro Takamune (UTokyo), Daichi Kitamura (Kagawa-NICT), Hiroshi Saruwatari (UTokyo), Rintaro Ikeshita, Tomohiro Nakatani (NTT)  EA2018-127 SIP2018-133 SP2018-89 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This paper focuses on independent positive semidefinite tensor analysis (IPSDTA), which is a technique for over-determined blind source separation. IPSDTA alternately updates demixing matrices and source models for minimizing the cost function. However, theoretical convergence of the conventional optimization algorithm has not been discussed. In this paper, we point out that the update step of the demixing matrix in the conventional algorithm lacks the theoretical convergence. Also, a new convergence-guaranteed optimization algorithm based on vectorwise coordinate descent is proposed. Experimental results show the validity of proposed IPSDTA regarding both the convergence and the performance of source separation. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Blind source separation / Positive semidefinite tensor factorization / Independent positive semidefinite tensor analysis / Convergence guarantee / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 495, EA2018-127, pp. 167-172, March 2019. | |
| Paper # | EA2018-127 | |
| Date of Issue | 2019-03-07 (EA, SIP, SP) | |
| 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 | EA2018-127 SIP2018-133 SP2018-89 |
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| Conference Information | |
| Committee | EA SIP SP |
| Conference Date | 2019-03-14 - 2019-03-15 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | i+Land nagasaki (Nagasaki-shi) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Engineering/Electro Acoustics, Signal Processing, Speech, and Related Topics |
| Paper Information | |
| Registration To | EA |
| Conference Code | 2019-03-EA-SIP-SP |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Convergence-guaranteed independent positive semidefinite tensor analysis for blind source separation |
| Sub Title (in English) | |
| Keyword(1) | Blind source separation |
| Keyword(2) | Positive semidefinite tensor factorization |
| Keyword(3) | Independent positive semidefinite tensor analysis |
| Keyword(4) | Convergence guarantee |
| Keyword(5) | |
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| 1st Author's Name | Kanta Fukushige |
| 1st Author's Affiliation | The University of Tokyo (UTokyo) |
| 2nd Author's Name | Norihiro Takamune |
| 2nd Author's Affiliation | The University of Tokyo (UTokyo) |
| 3rd Author's Name | Daichi Kitamura |
| 3rd Author's Affiliation | National Institute of Technology, Kagawa College (Kagawa-NICT) |
| 4th Author's Name | Hiroshi Saruwatari |
| 4th Author's Affiliation | The University of Tokyo (UTokyo) |
| 5th Author's Name | Rintaro Ikeshita |
| 5th Author's Affiliation | NTT Communication Science Laboratories (NTT) |
| 6th Author's Name | Tomohiro Nakatani |
| 6th Author's Affiliation | NTT Communication Science Laboratories (NTT) |
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| Speaker | Author-1 |
| Date Time | 2019-03-14 15:15:00 |
| Presentation Time | 25 minutes |
| Registration for | EA |
| Paper # | EA2018-127, SIP2018-133, SP2018-89 |
| Volume (vol) | vol.118 |
| Number (no) | no.495(EA), no.496(SIP), no.497(SP) |
| Page | pp.167-172 |
| #Pages | 6 |
| Date of Issue | 2019-03-07 (EA, SIP, SP) |
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