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    Improving model-based convolutive blind source separation techniques via bootstrap

    Chandna, Swati and Wang, W. (2014) Improving model-based convolutive blind source separation techniques via bootstrap. Proceedings of the IEEE Statistical Signal Processing Workshop, 2014 , pp. 424-427.

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    Blind source separation for underdetermined reverberant mixtures is often achieved by assuming a statistical model for cues of interest where the unknown parameters of the statistical model depend on hidden variables. Here, the expectation-maximization (EM) algorithm is employed to compute maximum-likelihood estimates of the unknown model parameters. A by-product of the EM algorithm is a time-frequency (T-F) mask which allows the estimation of the target source from the given mixture. In this paper, we propose the idea of bootstrap averaging to improve separation quality from mixtures recorded under reverberant conditions. Our experiments on real speech mixture signals show an increase in the signal-to-distortion ratio (SDR) over a stateof- the-art baseline algorithm, to our knowledge, currently, the best performing technique in this class of methods.


    Item Type: Article
    Additional Information: (c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    School: School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Swati Chandna
    Date Deposited: 12 Jan 2018 08:40
    Last Modified: 14 Jun 2021 18:45


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