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A Blind Source Separation Algorithm with Linear Prediction Filters

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Advances in Neural Networks – ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3173))

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Abstract

In this paper the theoretical analysis about why to be able to guarantee source separation by blind source separation (BSS) algorithms after linear prediction filtering is presented, and the concept of the linear prediction filter (LPF) application is illustrated. The simulation results verify the derivation of this paper and show that linear prediction analysis can be used in blind source separation for instantaneous mixtures and convolutive mixtures. For the convolutive mixtures, when the separated speech quality is improved, the separation performance is reduced a little.

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References

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Author information

Authors and Affiliations

  1. National Key Lab of Integrated Service Network, Xidian University, Xi’an, Shaan Xi, 710071, China

    Zhijin Zhao & Jiandong Li

  2. Hangzhou Institute of Electronics Engineering, Hangzhou, Zhe Jiang, 310018, China

    Zhijin Zhao & Fei Mei

Authors
  1. Zhijin Zhao
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  2. Fei Mei
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  3. Jiandong Li
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Zhao, Z., Mei, F., Li, J. (2004). A Blind Source Separation Algorithm with Linear Prediction Filters. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_113

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  • DOI: https://doi.org/10.1007/978-3-540-28647-9_113

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22841-7

  • Online ISBN: 978-3-540-28647-9

  • eBook Packages: Springer Book Archive

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