| bibtype |
J -
Journal Article
|
| ARLID |
0542013 |
| utime |
20240103225735.0 |
| mtime |
20210429235959.9 |
| SCOPUS |
85103266969 |
| WOS |
000645052600001 |
| DOI |
10.1109/TSP.2021.3068626 |
| title
(primary) (eng) |
Dynamic Independent Component/Vector Analysis: Time-Variant Linear Mixtures Separable by Time-Invariant Beamformers |
| specification |
| page_count |
16 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256727 |
| ISSN |
1053-587X |
| title
|
IEEE Transactions on Signal Processing |
| volume_id |
69 |
| volume |
1 (2021) |
| page_num |
2158-2173 |
|
| keyword |
Blind Source Separation |
| keyword |
Blind Source Extraction |
| keyword |
Independent Vector Analysis |
| author
(primary) |
| ARLID |
cav_un_auth*0230113 |
| name1 |
Koldovský |
| name2 |
Z. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0350114 |
| name1 |
Kautský |
| name2 |
V. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0101212 |
| name1 |
Tichavský |
| name2 |
Petr |
| institution |
UTIA-B |
| full_dept (cz) |
Stochastická informatika |
| full_dept |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department |
SI |
| full_dept |
Department of Stochastic Informatics |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0408584 |
| name1 |
Čmejla |
| name2 |
J. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0050739 |
| name1 |
Málek |
| name2 |
J. |
| country |
CZ |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA20-17720S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0396617 |
|
| abstract
(eng) |
A novel extension of Independent Component and Independent Vector Analysis for blind extraction/separation of one or several sources from time-varying mixtures is proposed. The mixtures are assumed to be separable source-by-source in series or in parallel based on a recently proposed mixing model that allows for the movements of the desired source while the separating beamformer is time-invariant. The popular FastICA algorithm is extended for these mixtures in one-unit, symmetric and block-deflation variants. The algorithms are derived within a unified framework so that they are applicable in the real-valued as well as complex-valued domains, and jointly to several mixtures, similar to Independent Vector Analysis. Performance analysis of the one-unit algorithm is provided, it shows its asymptotic efficiency under the given mixing and statistical models. Numerical simulations corroborate the validity of the analysis, confirm the usefulness of the algorithms in separation of moving sources, and show the superior speed of convergence and ability to separate super-Gaussian as well as sub-Gaussian signals. |
| result_subspec |
WOS |
| RIV |
BI |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20201 |
| reportyear |
2022 |
| num_of_auth |
5 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0320294 |
| confidential |
S |
| mrcbC86 |
2 Article Engineering Electrical Electronic |
| mrcbC91 |
C |
| mrcbT16-e |
ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-j |
1.67 |
| mrcbT16-s |
2.682 |
| mrcbT16-D |
Q1 |
| mrcbT16-E |
Q1* |
| arlyear |
2021 |
| mrcbU14 |
85103266969 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000645052600001 WOS |
| mrcbU63 |
cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 69 č. 1 2021 2158 2173 |
|