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[DL輪読会]Discriminative Learning for Monaural Speech Separation Using Deep Embedding Features
1. 1
DEEP LEARNING JP
[DL Papers]
http://deeplearning.jp/
Discriminative Learning for Monaural Speech
Separation Using Deep Embedding Features
Hiroshi Sekiguchi, Morikawa Lab
2. 書誌情報
• “Discriminative Learning for Monaural Speech Separation Using Deep
Embedding Features” (Interspeech 2019)
Author: Cunhang Fan, Bin Liu, Jianhua Tao, Jiangyan Yi, Zhengqi Wen
NLPR, Institute of Automation, Chinese Academy of Science, Beijing China
• 概要:
– モノラル信号の重畳音声分離を,最近注目の2手法、Deep Clusteringと
Utterance Permutation Invariant Trainingの良いとこ取りをし,かつ, end-to-
endでの学習を行い,単体の手法よりも“不特定話者”の複数音声分離性能を
上げた. 注)”不特定話者”とは、学習に含まれない話者のこと
– さらに,Discriminative Learningを追加して,分離性能を上げた.
• 動機
– 研究関連分野の論文レビュー 2
17. 参考文献
• J. R. Hershey, Z. Chen, J. L. Roux, and S. Watanabe, “Deep clustering:
Discriminative embeddings for segmentation and separation," in IEEE
International Conference on Acoustics, Speech and Signal Processing, 2016,
• M. Kolbæk, D. Yu, Z. Tan, J. Jensen, M. Kolbaek, D. Yu, Z. Tan, and J. Jensen,
“Multitalker speech separation with utterance-level permutation invariant
training of deep recurrent neural networks,” IEEE/ACM Transactions on
Audio, Speech and Language Processing (TASLP), vol. 25, no. 10, pp. 1901–
1913, 2017
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