| bibtype |
J -
Journal Article
|
| ARLID |
0531483 |
| utime |
20240103224313.6 |
| mtime |
20200810235959.9 |
| SCOPUS |
85089296542 |
| WOS |
000556759700004 |
| DOI |
10.1109/TSP.2020.3009507 |
| title
(primary) (eng) |
Performance Bounds for Complex-Valued Independent Vector Analysis |
| specification |
| page_count |
10 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256727 |
| ISSN |
1053-587X |
| title
|
IEEE Transactions on Signal Processing |
| volume_id |
68 |
| volume |
1 (2020) |
| page_num |
4258-4267 |
|
| keyword |
Blind source separation |
| keyword |
independent component/vector analysis |
| keyword |
Cramér-Rao lower bound, |
| author
(primary) |
| ARLID |
cav_un_auth*0350114 |
| name1 |
Kautský |
| name2 |
V. |
| country |
CZ |
| share |
60 |
|
| 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 |
| share |
15 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0230113 |
| name1 |
Koldovský |
| name2 |
Z. |
| country |
CZ |
| share |
15 |
|
| author
|
| ARLID |
cav_un_auth*0394533 |
| name1 |
Adali |
| name2 |
T. |
| country |
US |
| share |
10 |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA17-00902S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0345929 |
|
| project |
| project_id |
GA20-17720S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0395418 |
|
| abstract
(eng) |
Independent Vector Analysis (IVA) is a method for joint Blind Source Separation of multiple datasets with wide area of applications including audio source separation, biomedical data analysis, etc. In this paper, identification conditions and Cramér-Rao Lower Bound (CRLB) on the achievable accuracy are derived for the complex-valued case involving circular and non-circular signals and correlated and uncorrelated datasets.The identification conditions describe when independent sources can be separated from their linear mixture in the statistically consistent manner. The CRLB shows how non-Gaussianty, non-circularity of sources and statistical dependence between datasets influence the attainable separation accuracy. Examples presented in the experimental part confirm the validity of the CRLB. Also, they show certain gap between the attainable accuracy and performance of state-of-the-art algorithms,especially, in case of highlynon-circular signals. Hence, there is a room for possible improvements.\n |
| result_subspec |
WOS |
| RIV |
JD |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20201 |
| reportyear |
2021 |
| num_of_auth |
4 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0310654 |
| confidential |
S |
| mrcbC86 |
3+4 Article Engineering Electrical Electronic |
| mrcbC91 |
C |
| mrcbT16-e |
ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-i |
8.68988 |
| mrcbT16-j |
1.701 |
| mrcbT16-s |
1.638 |
| mrcbT16-B |
91.464 |
| mrcbT16-D |
Q1* |
| mrcbT16-E |
Q1 |
| arlyear |
2020 |
| mrcbU14 |
85089296542 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000556759700004 WOS |
| mrcbU63 |
cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 68 č. 1 2020 4258 4267 |
|