| bibtype |
J -
Journal Article
|
| ARLID |
0332915 |
| utime |
20240111140729.5 |
| mtime |
20091202235959.9 |
| DOI |
10.1016/j.sigpro.2009.04.021 |
| title
(primary) (eng) |
Blind Separation of Piecewise Stationary NonGaussian Sources |
| specification |
| page_count |
15 s. |
| media_type |
www |
|
| serial |
| ARLID |
cav_un_epca*0255076 |
| ISSN |
0165-1684 |
| title
|
Signal Processing |
| volume_id |
89 |
| volume |
12 (2009) |
| page_num |
2570-2584 |
| publisher |
|
|
| title
(cze) |
Slepá separace po částech stacionárních negaussovských zdrojů |
| keyword |
Independent component analysis |
| keyword |
blind source separation |
| keyword |
Cramer-Rao lower bound |
| author
(primary) |
| ARLID |
cav_un_auth*0108100 |
| name1 |
Koldovský |
| name2 |
Zbyněk |
| 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*0050739 |
| name1 |
Málek |
| name2 |
J. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0101212 |
| name1 |
Tichavský |
| name2 |
Petr |
| 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*0238913 |
| name1 |
Deville |
| name2 |
Y. |
| country |
FR |
|
| author
|
| ARLID |
cav_un_auth*0238914 |
| name1 |
Hosseini |
| name2 |
S. |
| country |
FR |
|
| source |
|
| cas_special |
| project |
| project_id |
1M0572 |
| agency |
GA MŠk |
| ARLID |
cav_un_auth*0001814 |
|
| project |
| project_id |
GA102/09/1278 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0253174 |
|
| project |
| project_id |
GA102/07/P384 |
| agency |
GA ČR |
| country |
CZ |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
We address Independent Component Analysis (ICA) of piecewise stationary and nonGaussian signals and propose a novel ICA algorithm called Block EFICA that is based on this generalized model of signals. The method is a further extension of the popular nonGaussianity-based FastICA algorithm and of its recently optimized variant called EFICA. In contrast to these methods, Block EFICA is developed to effectively exploit varying distribution of signals, thus, also their varying variance in time (nonstationarity) or, more precisely, in time-intervals (piecewise stationarity). In theory, the accuracy of the method asymptotically approaches Cramer-Rao lower bound (CRLB) under common assumptions when variance of the signals is constant. On the other hand, the performance is practically close to the CLRB even when variance of the signals is changing. |
| abstract
(cze) |
V práci je navržen algoritmus pro slepou separaci lineární okamžité směsi nezávislých po částech stacionárních negaussovských zdrojů. Algoritmus je nazván "bloková EFICA" a je rozšířením a zobecněním dřívějších algorimů FastICA a EFICA. V případě, že separované signálz mají konstantní varianci, dosahuje přesnost separace asymptoticky příslušné Rao-Cramerovy meze. Přínos algoritmu je ukázán na separaci okamžité směsi zvukových signálů. |
| reportyear |
2010 |
| RIV |
BB |
| permalink |
http://hdl.handle.net/11104/0178031 |
| mrcbT16-f |
1.283 |
| mrcbT16-g |
0.261 |
| mrcbT16-h |
7.7 |
| mrcbT16-i |
0.0127 |
| mrcbT16-j |
0.528 |
| mrcbT16-k |
3980 |
| mrcbT16-l |
261 |
| mrcbT16-q |
68 |
| mrcbT16-s |
0.991 |
| mrcbT16-y |
25.02 |
| mrcbT16-x |
1.77 |
| arlyear |
2009 |
| mrcbU56 |
pdf |
| mrcbU63 |
cav_un_epca*0255076 Signal Processing 0165-1684 1872-7557 Roč. 89 č. 12 2009 2570 2584 Elsevier |
|