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| Presentation | 2019-12-12 14:25 Performance improvement of speech enhancement network by multitask learning including noise information Haruki Tanaka (NITTC), Yosuke Sugiura, Nozomiko Yasui, Tetsuya Shimamura (Saitama Univ.), Ryoichi Miyazaki (NITTC)  EA2019-70 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In the signal processing field, there is a growing interest in speech enhancement.Recently, a lot of speech enhancement methods using deep learning have been proposed. Mostly, SEGAN and Wave-U-Net, which use waveform data as input/output and automatic encoder for network, have shown high performance. In this paper, we propose a speech enhancement method based on an architecture mutually constraining latent vectors of two autoencoders simultaneously learning speech and noise information contained in noise mixed speech. The input of our network uses noise-contaminated speech, and one auto-encoder outputs clean speech and the other outputs noise information. In the experiment, we show that the proposed method exceeded the score of SEGAN in the objective evaluation scale by PESQ, CSIG, and COVL. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Speech Enhancement / Deep Learning / Autoencoder / Malti-task Learning / Generative Adversarial Nets / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 334, EA2019-70, pp. 31-36, Dec. 2019. | |
| Paper # | EA2019-70 | |
| Date of Issue | 2019-12-05 (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 | EA2019-70 |
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| Conference Information | |
| Committee | EA |
| Conference Date | 2019-12-12 - 2019-12-13 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kyushu Inst. Tech. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Engineering/Electro Acoustics, and Related Topics |
| Paper Information | |
| Registration To | EA |
| Conference Code | 2019-12-EA |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Performance improvement of speech enhancement network by multitask learning including noise information |
| Sub Title (in English) | |
| Keyword(1) | Speech Enhancement |
| Keyword(2) | Deep Learning |
| Keyword(3) | Autoencoder |
| Keyword(4) | Malti-task Learning |
| Keyword(5) | Generative Adversarial Nets |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Haruki Tanaka |
| 1st Author's Affiliation | National Institute of Technology, Tokuyama College (NITTC) |
| 2nd Author's Name | Yosuke Sugiura |
| 2nd Author's Affiliation | Saitama University (Saitama Univ.) |
| 3rd Author's Name | Nozomiko Yasui |
| 3rd Author's Affiliation | Saitama University (Saitama Univ.) |
| 4th Author's Name | Tetsuya Shimamura |
| 4th Author's Affiliation | Saitama University (Saitama Univ.) |
| 5th Author's Name | Ryoichi Miyazaki |
| 5th Author's Affiliation | National Institute of Technology, Tokuyama College (NITTC) |
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| Speaker | Author-1 |
| Date Time | 2019-12-12 14:25:00 |
| Presentation Time | 25 minutes |
| Registration for | EA |
| Paper # | EA2019-70 |
| Volume (vol) | vol.119 |
| Number (no) | no.334 |
| Page | pp.31-36 |
| #Pages | 6 |
| Date of Issue | 2019-12-05 (EA) |
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