bibtype V - Research Report
ARLID 0480768
utime 20240103214849.0
mtime 20171102235959.9
title (primary) (eng) Adaptive Blind Separation of Instantaneous Linear Mixtures of Independent Sources
publisher
place Praha
name ÚTIA AV ČR v.v.i
pub_time 2016
specification
page_count 10 s.
media_type P
edition
name Research Report
volume_id 2360
keyword blind separation
keyword algorithms
keyword block gaussian separation
author (primary)
ARLID cav_un_auth*0319418
name1 Šembera
name2 Ondřej
full_dept (cz) Stochastická informatika
full_dept (eng) Department of Stochastic Informatics
department (cz) SI
department (eng) SI
institution UTIA-B
full_dept Department of Stochastic Informatics
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101212
name1 Tichavský
name2 Petr
full_dept (cz) Stochastická informatika
full_dept Department of Stochastic Informatics
department (cz) SI
department SI
institution UTIA-B
full_dept Department of Stochastic Informatics
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0108100
name1 Koldovský
name2 Zbyněk
full_dept (cz) Stochastická informatika
full_dept Department of Stochastic Informatics
department (cz) SI
department SI
institution UTIA-B
full_dept Department of Stochastic Informatics
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2017/SI/tichavsky-0480768.pdf
cas_special
project
ARLID cav_un_auth*0352654
project_id FV10645
agency GA MPO
abstract (eng) 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.
RIV BI
FORD0 10000
FORD1 10300
FORD2 10307
reportyear 2018
mrcbC52 4 O 4o 20231122142757.1
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0276463
confidential U
arlyear 2016
mrcbTft \nSoubory v repozitáři: 0480768.pdf
mrcbU10 2016
mrcbU10 Praha ÚTIA AV ČR v.v.i