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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

BI - Akustika a kmity

OECD FORD obor

—

Projekt

<a href="/cs/project/GA14-13713S" target="_blank" >GA14-13713S: Metody dekompozice tenzorů a jejich aplikace</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Latent Variable Analysis and Signal Separation

ISBN

978-3-319-22482-4

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

8

Strana od-do

304-311

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Liberec

Datum konání akce

25. 8. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000363785500035