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Phase Constraint and Deep Neural Network for Speech Separation

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Advances in Neural Networks - ISNN 2017 (ISNN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10262))

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Abstract

The phase response of speech is an important part in speech separation. In this paper, we apply the complex mask to the speech separation. It both enhances the magnitude and phase of speech. Specifically, we use a deep neural network to estimate the complex mask of two sources. And considering the importance of the phase, we also explore a phase constraint objective function, which can ensure the phase of the sum of estimated sources that is close to the phase of the mixture. We demonstrate the efficiency of the method on the TIMIT speech corpus for single channel speech separation.

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Author information

Authors and Affiliations

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

    Zhuangguo Miao & Xiaohong Ma

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

    Shuxue Ding

Authors
  1. Zhuangguo Miao
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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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Miao, Z., Ma, X., Ding, S. (2017). Phase Constraint and Deep Neural Network for Speech Separation. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_32

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  • DOI: https://doi.org/10.1007/978-3-319-59081-3_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59080-6

  • Online ISBN: 978-3-319-59081-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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