Paper Abstract and Keywords |
Presentation |
2021-03-05 09:45
Improved Speech Separation Performance from Monaural Mixed Speech Based on Deep Embedding Network Shaoxiang Dang, Tetsuya Matsumoto, Hiroaki Kudo (Nagoya Univ.), Yoshinori Takeuchi (Daido Univ.) PRMU2020-85 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Speech separation refers to the separation of utterances in which multiple people are speaking simultaneously. The idea of deep clustering (DC) is put forward by using a deep embedding network to embed audio data in the underlying manifold, and data with similar property gathers tightly in the embedding space. Then this model uses a clustering algorithm because the clustering algorithm can easily separate distributed data. Regarding the learning process, the model is supervised by an ideal affinity matrix constructed of binary masks of annotation data. However, the binary mask gives a bottleneck to the entire system since the same position of bins in masks are assigned to 0 or 1 according to the contribution of individual utterances to mixed spectrograms. Thus, we propose an extended two-stage version of network based on the deep embedding. The network can eliminate the shortcomings by using various more accurate masks. We employ DC as our first stage, and conduct a permutation invariant training approach to prevent permutation problem in the second stage. As a result, the results according to our experiment outperforms the original DC model by 1.55dB in SNR by 4.45dB in SDR, 4.41dB in SDRi, 0.16 in STOI, and 0.3 in PESQ on average. We also observe that the proposed method can recover the defects in the spectrograms brought in by DC. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
speech separation / deep embedding network / monaural speech separation / permutation invariant training / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 409, PRMU2020-85, pp. 91-96, March 2021. |
Paper # |
PRMU2020-85 |
Date of Issue |
2021-02-25 (PRMU) |
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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PRMU2020-85 |
Conference Information |
Committee |
PRMU IPSJ-CVIM |
Conference Date |
2021-03-04 - 2021-03-05 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Computer Vision and Pattern Recognition for specific environment |
Paper Information |
Registration To |
PRMU |
Conference Code |
2021-03-PRMU-CVIM |
Language |
English |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Improved Speech Separation Performance from Monaural Mixed Speech Based on Deep Embedding Network |
Sub Title (in English) |
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speech separation |
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deep embedding network |
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monaural speech separation |
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permutation invariant training |
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1st Author's Name |
Shaoxiang Dang |
1st Author's Affiliation |
Nagoya University (Nagoya Univ.) |
2nd Author's Name |
Tetsuya Matsumoto |
2nd Author's Affiliation |
Nagoya University (Nagoya Univ.) |
3rd Author's Name |
Hiroaki Kudo |
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Nagoya University (Nagoya Univ.) |
4th Author's Name |
Yoshinori Takeuchi |
4th Author's Affiliation |
Daido University (Daido Univ.) |
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Speaker |
Author-1 |
Date Time |
2021-03-05 09:45:00 |
Presentation Time |
15 minutes |
Registration for |
PRMU |
Paper # |
PRMU2020-85 |
Volume (vol) |
vol.120 |
Number (no) |
no.409 |
Page |
pp.91-96 |
#Pages |
6 |
Date of Issue |
2021-02-25 (PRMU) |
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