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| Presentation | 2018-03-19 10:00 Experimental Evaluation of Multichannel Audio Source Separation Based on IDLMA Daichi Kitamura, Hayato Sumino, Norihiro Takamune, Shinnosuke Takamichi, Hiroshi Saruwatari (Univ. of Tokyo), Nobutaka Ono (Tokyo Metropolitan Univ.)  EA2017-104 SIP2017-113 SP2017-87 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this paper, we propose a new informed multichannel audio source separation called independent deeply learned matrix analysis (IDLMA). IDLMA is a unified algorithm of conventional blind source separation, independent low-rank matrix analysis, and a supervised learning method based on deep neural networks (DNN) and can be interpreted as a natural informed extension of the independence-based source separation theory. Although a source model is estimated by pre-trained sourcewise DNN, a spatial model can blindly be estimated by statistical independence between sources. The experiment using music signals shows the efficacy of IDLMA compared with the conventional DNN-based techniques. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | multichannel audio source separation / independent component analysis / deep neural networks / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 515, EA2017-104, pp. 13-20, March 2018. | |
| Paper # | EA2017-104 | |
| Date of Issue | 2018-03-12 (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 | EA2017-104 SIP2017-113 SP2017-87 |
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| Conference Information | |
| Committee | SIP EA SP MI |
| Conference Date | 2018-03-19 - 2018-03-20 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Speech, Engineering/Electro Acoustics, Signal Processing, and Related Topics [SIP, EA, SP]/ Medical Image Engineering, Analysis, Recognition, etc. [MI] |
| Paper Information | |
| Registration To | EA |
| Conference Code | 2018-03-SIP-EA-SP-MI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Experimental Evaluation of Multichannel Audio Source Separation Based on IDLMA |
| Sub Title (in English) | |
| Keyword(1) | multichannel audio source separation |
| Keyword(2) | independent component analysis |
| Keyword(3) | deep neural networks |
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| 1st Author's Name | Daichi Kitamura |
| 1st Author's Affiliation | The University of Tokyo (Univ. of Tokyo) |
| 2nd Author's Name | Hayato Sumino |
| 2nd Author's Affiliation | The University of Tokyo (Univ. of Tokyo) |
| 3rd Author's Name | Norihiro Takamune |
| 3rd Author's Affiliation | The University of Tokyo (Univ. of Tokyo) |
| 4th Author's Name | Shinnosuke Takamichi |
| 4th Author's Affiliation | The University of Tokyo (Univ. of Tokyo) |
| 5th Author's Name | Hiroshi Saruwatari |
| 5th Author's Affiliation | The University of Tokyo (Univ. of Tokyo) |
| 6th Author's Name | Nobutaka Ono |
| 6th Author's Affiliation | Tokyo Metropolitan University (Tokyo Metropolitan Univ.) |
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| Speaker | Author-1 |
| Date Time | 2018-03-19 10:00:00 |
| Presentation Time | 25 minutes |
| Registration for | EA |
| Paper # | EA2017-104, SIP2017-113, SP2017-87 |
| Volume (vol) | vol.117 |
| Number (no) | no.515(EA), no.516(SIP), no.517(SP) |
| Page | pp.13-20 |
| #Pages | 8 |
| Date of Issue | 2018-03-12 (EA, SIP, SP) |
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