Second-Order Blind Signal Separation for Convolutive Mixtures Using Conjugate Gradient

H.H. Dam, Antonio Cantoni, S.E. Nordholm, K.L. Teo

    Research output: Contribution to journalArticle

    12 Citations (Scopus)
    352 Downloads (Pure)

    Abstract

    This letter presents a new computational procedure for the second-order gradient-based blind signal separation (BSS) problem with convolutive mixtures that has improved convergence characteristics over the steepest descent algorithm. The BSS problem is formulated as a constrained optimization problem with complex unmixing weight matrices where the constraints are formulated to overcome the permutation effects. This problem is then transformed into an unconstrained optimization problem, so that the conjugate gradient algorithm can be applied. The convergence of the proposed procedure is compared with the steepest descent algorithms in real and simulated environments.
    Original languageEnglish
    Pages (from-to)79-82
    JournalIEEE Signal Processing Letters
    Volume15
    DOIs
    Publication statusPublished - 2008

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