| bibtype |
J -
Journal Article
|
| ARLID |
0563805 |
| utime |
20230323095823.1 |
| mtime |
20221109235959.9 |
| SCOPUS |
85141497959 |
| WOS |
000880643100006 |
| DOI |
10.1109/TSP.2022.3216106 |
| title
(primary) (eng) |
Double Nonstationarity: Blind Extraction of Independent Nonstationary Vector/Component from Nonstationary Mixtures - Algorithms |
| specification |
| page_count |
15 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256727 |
| ISSN |
1053-587X |
| title
|
IEEE Transactions on Signal Processing |
| volume_id |
70 |
| volume |
1 (2022) |
| page_num |
5102-5116 |
|
| keyword |
blind source extraction |
| keyword |
non-stationarity |
| keyword |
speech processing |
| 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. |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA20-17720S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0395418 |
|
| abstract
(eng) |
In this article, nonstationary mixing and source models are combined for developing new fast and accurate algorithms for Independent Component or Vector Extraction (ICE/IVE), one of which stands for a new extension of the well-known FastICA. This model allows for a moving source-of-interest (SOI) whose distribution on short intervals can be (non-)circular (non-)Gaussian. A particular Gaussian source model assuming tridiagonal covariance matrix structures is proposed. It is shown to be beneficial in the frequency-domain speaker extraction problem. The algorithms are verified in simulations. In comparison to the state-of-the-art algorithms, they show superior performance in terms of convergence speed and extraction accuracy. |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20201 |
| reportyear |
2023 |
| num_of_auth |
3 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0336407 |
| confidential |
S |
| mrcbC86 |
n.a. Article Engineering Electrical Electronic |
| mrcbC91 |
C |
| mrcbT16-e |
ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-j |
1.942 |
| mrcbT16-s |
2.503 |
| mrcbT16-D |
Q1 |
| mrcbT16-E |
Q1* |
| arlyear |
2022 |
| mrcbU14 |
85141497959 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000880643100006 WOS |
| mrcbU63 |
cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 70 č. 1 2022 5102 5116 |
|