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Blind Separation of Mixtures of Piecewise AR(1) Processes and Model Mismatch

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F15%3A00447196" target="_blank" >RIV/67985556:_____/15:00447196 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/978-3-319-22482-4_35" target="_blank" >http://dx.doi.org/10.1007/978-3-319-22482-4_35</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-319-22482-4_35" target="_blank" >10.1007/978-3-319-22482-4_35</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Blind Separation of Mixtures of Piecewise AR(1) Processes and Model Mismatch

  • Original language description

    Modeling real-world acoustic signals and namely speech signals as piecewise stationary random processes is a possible approach to blind separation of linear mixtures of such signals. In this paper, the piecewise AR(1) modeling is studied and is comparedto the more common piecewise AR(0) modeling, which is known under the names Block Gaussian SEParation (BGSEP) and Block Gaussian Likelihood (BGL). The separation based on the AR(0) modeling uses an approximate joint diagonalization (AJD) of covariance matrices of the mixture with lag 0, computed at epochs (intervals) of stationarity of the separated signals. The separation based on the AR(1) modeling uses the covariances of lag 0 and covariances of lag 1 jointly. For this model, we derive an approximateCram´er-Rao lower bound on the separation accuracy for estimation based on the full set of the statistics (covariance matrices of lag 0 and lag 1) and covariance matrices with lag 0 only. The bounds show the condition when AR(1) modeling

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    BI - Acoustics and oscillation

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GA14-13713S" target="_blank" >GA14-13713S: Tensor Decomposition Methods and Their Applications</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2015

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Latent Variable Analysis and Signal Separation

  • ISBN

    978-3-319-22482-4

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    304-311

  • Publisher name

    Springer

  • Place of publication

    Heidelberg

  • Event location

    Liberec

  • Event date

    Aug 25, 2015

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000363785500035