Adaptive Blind Separation of Instantaneous Linear Mixtures of Independent Sources
The result's identifiers
Result code in 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>
Alternative codes found
RIV/46747885:24220/17:00004530
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Adaptive Blind Separation of Instantaneous Linear Mixtures of Independent Sources
Original language description
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.
Czech name
—
Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/GA17-00902S" target="_blank" >GA17-00902S: Advanded Joint Blind Source Separation Methods</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
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, 13th International Conference, LVA/ICA 2017
ISBN
978-3-319-53546-3
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
10
Pages from-to
172-181
Publisher name
Springer
Place of publication
Cham
Event location
Grenoble
Event date
Feb 21, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000418581400017