Constrained Complex-Valued ICA without Permutation Ambiguity Based on Negentropy Maximization View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

David Hutchison , Takeo Kanade , Josef Kittler , Jon M. Kleinberg , Friedemann Mattern , John C. Mitchell , Moni Naor , Oscar Nierstrasz , C. Pandu Rangan , Bernhard Steffen , Madhu Sudan , Demetri Terzopoulos , Doug Tygar , Moshe Y. Vardi , Gerhard Weikum , Qiu-Hua Lin , Li-Dan Wang , Jian-Gang Lin , Xiao-Feng Gong

ABSTRACT

Complex independent component analysis (ICA) has found utility in separation of complex-valued signals such as communications, functional magnetic resonance imaging, and frequency-domain speeches. However, permutation ambiguity is a main problem of complex ICA for order-sensitive applications, e.g., frequency-domain speech separation. This paper proposes a semi-blind complex ICA algorithm based on negentropy maximization. The magnitude correlation of a source signal is utilized to constrain the separation process. As a result, the complex-valued signals are separated without permutation. Experiments with synthetic complex-valued signals, synthetic speech signals, and recorded speech signals are performed. The results demonstrate that the proposed algorithm can not only solve the permutation problem, but also achieve slightly improved separation compared to the standard blind algorithm. More... »

PAGES

271-278

References to SciGraph publications

  • 2005. Frequency-Domain Blind Source Separation in SPEECH ENHANCEMENT
  • Book

    TITLE

    Latent Variable Analysis and Signal Separation

    ISBN

    978-3-642-15994-7
    978-3-642-15995-4

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-15995-4_34

    DOI

    http://dx.doi.org/10.1007/978-3-642-15995-4_34

    DIMENSIONS

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