Blind separation of underdetermined linear mixtures based on source nonstationarity and AR(1) modeling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00458485" target="_blank" >RIV/67985556:_____/16:00458485 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Blind separation of underdetermined linear mixtures based on source nonstationarity and AR(1) modeling

Popis výsledku v původním jazyce

The problem of blind separation of underdetermined instantaneous mixtures of independent signals is addressed through a method relying on nonstationarity of the original signals. The signals are assumed to be piecewise stationary with varying variances in different epochs. In comparison with previous works, in this paper it is assumed that the signals are not id. in each epoch, but obey a first-order autoregressive model. This model was shown to be more appropriate for blind separation of natural speech signals. A separation method is proposed that is nearly statistically efficient (approaching the corresponding Cram´er-Rao lower bound), if the separated signals obey the assumed model. In the case of natural speech signals, the method is shown to have separation accuracy better than the state-of-the-art methods.

Název v anglickém jazyce

Blind separation of underdetermined linear mixtures based on source nonstationarity and AR(1) modeling

Popis výsledku anglicky

The problem of blind separation of underdetermined instantaneous mixtures of independent signals is addressed through a method relying on nonstationarity of the original signals. The signals are assumed to be piecewise stationary with varying variances in different epochs. In comparison with previous works, in this paper it is assumed that the signals are not id. in each epoch, but obey a first-order autoregressive model. This model was shown to be more appropriate for blind separation of natural speech signals. A separation method is proposed that is nearly statistically efficient (approaching the corresponding Cram´er-Rao lower bound), if the separated signals obey the assumed model. In the case of natural speech signals, the method is shown to have separation accuracy better than the state-of-the-art methods.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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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

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Proocessing

ISBN

978-1-4799-9987-3

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

4323-4327

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Shanghai

Datum konání akce

20. 3. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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