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A Comparative Study of Blind Speech Separation Using Subspace Methods and Higher Order Statistics

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Book cover Signal Processing, Image Processing and Pattern Recognition (SIP 2009)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 61))

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Abstract

In this paper we report the results of a comparative study on blind speech signal separation approaches. Three algorithms, Oriented Principal Component Analysis (OPCA), High Order Statistics (HOS), and Fast Independent Component Analysis (Fast-ICA), are objectively compared in terms of signal-to-interference ratio criteria. The results of experiments carried out using the TIMIT and AURORA speech databases show that OPCA outperforms the other techniques. It turns out that OPCA can be used for blindly separating temporal signals from their linear mixtures without need for a pre-whitening step.

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Author information

Authors and Affiliations

  1. Institut National de la Recherche Scientifique-Energie, Matériaux, Télécommunications, University of Quebec, 800 de la Gauchetière O, H5A 1K6, Montréal, Qc, Canada

    Yasmina Benabderrahmane & Douglas O’Shaughnessy

  2. Université de Moncton, campus de Shippagan, E8S 1P6, NB, Canada

    Sid Ahmed Selouani & Habib Hamam

Authors
  1. Yasmina Benabderrahmane
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  2. Sid Ahmed Selouani
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  3. Douglas O’Shaughnessy
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  4. Habib Hamam
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Editor information

Editors and Affiliations

  1. University of Warsaw and Infobright Inc., Poland

    Dominik Ślęzak

  2. Indian Statistical Institute, India

    Sankar K. Pal

  3. University of Tasmania, 7001, Hobart, Australia

    Byeong-Ho Kang

  4. Lab. of Multimedia Information Technology (MMIT), East China Normal University (ECNU), P.O. Box, 200062, Shanghai, China

    Junzhong Gu

  5. Nagasaki University, Japan

    Hideo Kuroda

  6. Hannam University, 306-791, Daejeon, South Korea

    Tai-hoon Kim

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© 2009 Springer-Verlag Berlin Heidelberg

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Benabderrahmane, Y., Selouani, S.A., O’Shaughnessy, D., Hamam, H. (2009). A Comparative Study of Blind Speech Separation Using Subspace Methods and Higher Order Statistics. In: Ślęzak, D., Pal, S.K., Kang, BH., Gu, J., Kuroda, H., Kim, Th. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2009. Communications in Computer and Information Science, vol 61. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10546-3_15

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  • DOI: https://doi.org/10.1007/978-3-642-10546-3_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10545-6

  • Online ISBN: 978-3-642-10546-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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