| bibtype |
J -
Journal Article
|
| ARLID |
0356666 |
| utime |
20240103194855.7 |
| mtime |
20110228235959.9 |
| WOS |
000287316500014 |
| SCOPUS |
79951643186 |
| DOI |
10.1109/TSP.2010.2096221 |
| title
(primary) (eng) |
Weight adjusted tensor method for blind separation of underdetermined mixtures of nonstationary sources |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0256727 |
| ISSN |
1053-587X |
| title
|
IEEE Transactions on Signal Processing |
| volume_id |
59 |
| volume |
3 (2011) |
| page_num |
1037-1047 |
|
| keyword |
blind source separation |
| keyword |
tensor decomposition |
| keyword |
Cramer-Rao lower bound |
| author
(primary) |
| ARLID |
cav_un_auth*0101212 |
| name1 |
Tichavský |
| name2 |
Petr |
| 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*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 |
|
| 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 |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
In this paper, a novel algorithm to blindly separate an instantaneous linear underdetermined mixture of nonstationary sources is proposed. The separation is based on the working assumption that the sources are piecewise stationary with a different variance in each block. It proceeds in two steps: (1) estimating the mixing matrix, and (2) computing the optimum beamformer in each block to maximize the signal-to-interference ratio of each separated signal. Estimating the mixing matrix is accomplished through a specialized tensor decomposition of the set of sample covariance matrices of the received mixture in each block. It utilizes optimum weighting, which allows statistically efficient (CRB attaining) estimation provided that the data obey the assumed Gaussian piecewise stationary model. In simulations, performance of the algorithm is successfully tested on blind separation of 16 speech signals from 9 linear instantaneous mixtures of these signals. |
| reportyear |
2011 |
| RIV |
BB |
| mrcbC52 |
4 A 4a 20231122134443.5 |
| permalink |
http://hdl.handle.net/11104/0195127 |
| mrcbT16-e |
ENGINEERINGELECTRICALELECTRONIC |
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2.829 |
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0.421 |
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6.9 |
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0.05714 |
| mrcbT16-j |
1.249 |
| mrcbT16-k |
16727 |
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527 |
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151 |
| mrcbT16-s |
1.647 |
| mrcbT16-y |
30.74 |
| mrcbT16-x |
4.12 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
90.357 |
| mrcbT16-C |
88.776 |
| mrcbT16-D |
Q1* |
| mrcbT16-E |
Q1* |
| arlyear |
2011 |
| mrcbTft |
\nSoubory v repozitáři: tichavsky-0356666.pdf |
| mrcbU14 |
79951643186 SCOPUS |
| mrcbU34 |
000287316500014 WOS |
| mrcbU63 |
cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 59 č. 3 2011 1037 1047 |
|