Paper Abstract and Keywords |
Presentation |
2023-03-01 11:00
Analysis of Noisy-target Training for DNN-based speech enhancement and investigation towards its practical use Takuya Fujimura, Tomoki Toda (Nagoya Univ.) EA2022-112 SIP2022-156 SP2022-76 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Deep neural network (DNN)-based speech enhancement usually uses a clean speech as a training target. However, it is hard to collect large amounts of clean speech because its recording is very costly. To relax this limitation, we proposed Noisy-target Training (NyTT) that utilizes noisy speech as a training target. It has been experimentally shown that NyTT can train a DNN without clean speech. However, sufficient investigations have not been conducted to clarify the reason why NyTT works, its detailed property, and the effectiveness of utilizing large amounts of noisy speech. In this paper, we conduct various analyses to deepen our understanding of NyTT. Based on the property of NyTT, we also propose a refined method that performs higher-quality speech enhancement. Furthermore, we investigate whether using a huge amount of noisy speech is effective for improving speech enhancement performance. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Single channel speech enhancement / Deep Neural Network / Unsupervised learning / Behavior analysis / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 387, EA2022-112, pp. 221-226, Feb. 2023. |
Paper # |
EA2022-112 |
Date of Issue |
2023-02-21 (EA, SIP, SP) |
ISSN |
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) |
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EA2022-112 SIP2022-156 SP2022-76 |
Conference Information |
Committee |
SP IPSJ-SLP EA SIP |
Conference Date |
2023-02-28 - 2023-03-01 |
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(See Japanese page) |
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Paper Information |
Registration To |
EA |
Conference Code |
2023-02-SP-SLP-EA-SIP |
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Japanese |
Title (in Japanese) |
(See Japanese page) |
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(See Japanese page) |
Title (in English) |
Analysis of Noisy-target Training for DNN-based speech enhancement and investigation towards its practical use |
Sub Title (in English) |
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Keyword(1) |
Single channel speech enhancement |
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Deep Neural Network |
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Unsupervised learning |
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Behavior analysis |
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1st Author's Name |
Takuya Fujimura |
1st Author's Affiliation |
Nagoya University (Nagoya Univ.) |
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Tomoki Toda |
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Nagoya University (Nagoya Univ.) |
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Speaker |
Author-1 |
Date Time |
2023-03-01 11:00:00 |
Presentation Time |
20 minutes |
Registration for |
EA |
Paper # |
EA2022-112, SIP2022-156, SP2022-76 |
Volume (vol) |
vol.122 |
Number (no) |
no.387(EA), no.388(SIP), no.389(SP) |
Page |
pp.221-226 |
#Pages |
6 |
Date of Issue |
2023-02-21 (EA, SIP, SP) |
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