Adaptive Blind Separation of Instantaneous Linear Mixtures of Independent Sources

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00473144" target="_blank" >RIV/67985556:_____/17:00473144 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/46747885:24220/17:00004530

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-53547-0" target="_blank" >http://dx.doi.org/10.1007/978-3-319-53547-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-53547-0" target="_blank" >10.1007/978-3-319-53547-0</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Adaptive Blind Separation of Instantaneous Linear Mixtures of Independent Sources

Popis výsledku v původním jazyce

In many applications, there is a need to blindly separate independent sources from their linear instantaneous mixtures while the mixing matrix or source properties are slowly or abruptly changing in time. The easiest way to separate the data is to consider off-line estimation of the model parameters repeatedly in time shifting window. Another popular method is the stochastic natural gradient algorithm, which relies on non-Gaussianity of the separated signals and is adaptive by its nature. In this paper, we propose an adaptive version of two blind source separation algorithms which exploit non-stationarity of the original signals. The results indicate that the proposed algorithms slightly outperform the natural gradient in the trade-off between the algorithm’s ability to quickly adapt to changes in the mixing matrix and the variance of the estimate when the mixing is stationary.

Název v anglickém jazyce

Adaptive Blind Separation of Instantaneous Linear Mixtures of Independent Sources

Popis výsledku anglicky

In many applications, there is a need to blindly separate independent sources from their linear instantaneous mixtures while the mixing matrix or source properties are slowly or abruptly changing in time. The easiest way to separate the data is to consider off-line estimation of the model parameters repeatedly in time shifting window. Another popular method is the stochastic natural gradient algorithm, which relies on non-Gaussianity of the separated signals and is adaptive by its nature. In this paper, we propose an adaptive version of two blind source separation algorithms which exploit non-stationarity of the original signals. The results indicate that the proposed algorithms slightly outperform the natural gradient in the trade-off between the algorithm’s ability to quickly adapt to changes in the mixing matrix and the variance of the estimate when the mixing is stationary.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA17-00902S" target="_blank" >GA17-00902S: Pokročilé metody slepé separace podprostorů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

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, 13th International Conference, LVA/ICA 2017

ISBN

978-3-319-53546-3

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

10

Strana od-do

172-181

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Grenoble

Datum konání akce

21. 2. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000418581400017