| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0505138 |
| utime |
20240111141019.8 |
| mtime |
20190603235959.9 |
| SCOPUS |
85069501261 |
| WOS |
000482554005104 |
| DOI |
10.1109/ICASSP.2019.8683885 |
| title
(primary) (eng) |
Performance bound for blind extraction of non-Gaussian complex-valued vector component from Gaussian background |
| specification |
| page_count |
5 s. |
| media_type |
C |
|
| serial |
| ARLID |
cav_un_epca*0505137 |
| ISBN |
978-1-4799-8130-4 |
| title
|
2019 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2019 |
| page_num |
5287-5291 |
| publisher |
| place |
Brighton, UK |
| name |
IEEE |
| year |
2019 |
|
|
| keyword |
Blind Source Extraction |
| keyword |
Independent Component Analysis |
| keyword |
Independent Vector Analysis |
| author
(primary) |
| ARLID |
cav_un_auth*0355383 |
| share |
40 |
| name1 |
Kautský |
| name2 |
Václav |
| institution |
UTIA-B |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
SI |
| full_dept |
Department of Stochastic Informatics |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0230113 |
| share |
30 |
| name1 |
Koldovský |
| name2 |
Z. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0101212 |
| share |
30 |
| 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. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0345929 |
| project_id |
GA17-00902S |
| agency |
GA ČR |
|
| abstract
(eng) |
Independent Vector Extraction aims at the joint blind source extraction of K dependent signals of interest (SOI) from K mixtures (one signal from one mixture). Similarly to Independent Component/Vector Analysis (ICA/IVA), the SOIs are assumed to be independent of the other signals in the mixture. Compared to IVA, the (de-)mixing IVE model is reduced in the number of parameters for the extraction problem. The SOIs are assumed to be non-Gaussian or noncircular Gaussian, while the other signals are modeled as circular Gaussian. In this paper, a Cramer-Rao-Induced Bound (CRIB) for the achievable Interference-to-Signal Ratio (ISR) is derived for IVE. The bound is compared with similar bounds for ICA, IVA, and Independent Component Extraction (ICE). Numerical simulations show a good correspondence between the empirical results and the theory. |
| action |
| ARLID |
cav_un_auth*0375642 |
| name |
2019 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2019 |
| dates |
20190512 |
| mrcbC20-s |
20190517 |
| place |
Brighton |
| country |
GB |
|
| RIV |
BB |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20205 |
| reportyear |
2020 |
| num_of_auth |
3 |
| presentation_type |
PO |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0297073 |
| confidential |
S |
| mrcbC86 |
3+4 Proceedings Paper Acoustics|Engineering Electrical Electronic |
| arlyear |
2019 |
| mrcbU14 |
85069501261 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000482554005104 WOS |
| mrcbU56 |
343 kB |
| mrcbU63 |
cav_un_epca*0505137 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2019 978-1-4799-8130-4 5287 5291 Brighton, UK IEEE 2019 CFP19ICA-USB |
|