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Single Channel Speech Separation Using Deep Neural Network

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Advances in Neural Networks - ISNN 2017 (ISNN 2017)

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Abstract

Single channel speech separation (SCSS) is an important and challenging research problem and has received considerable interests in recent years. A supervised single channel speech separation method based on deep neural network (DNN) is proposed in this paper. We explore a new training strategy based on curriculum learning to enhance the robustness of DNN. In the training processing, the training samples firstly are sorted by the separation difficulties and then gradually introduced into DNN for training from easy to complex cases, which is similar to the learning principle of human brain. In addition, a strong discriminative objective function for reducing the source interference is designed by adding in the correlation coefficients and negentropy. The efficiency of the proposed method is substantiated by a monaural speech separation task using TIMIT corpus.

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Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Linlin Chen & Xiaohong Ma

  2. School of Computer Science and Engineering, University of Aizu Fukushima, Aizuwakamatsu, Japan

    Linlin Chen & Shuxue Ding

Authors
  1. Linlin Chen
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  2. Xiaohong Ma
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  3. Shuxue Ding
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Corresponding author

Correspondence to Xiaohong Ma .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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Chen, L., Ma, X., Ding, S. (2017). Single Channel Speech Separation Using Deep Neural Network. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_34

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  • DOI: https://doi.org/10.1007/978-3-319-59072-1_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59071-4

  • Online ISBN: 978-3-319-59072-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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