| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0458485 |
| utime |
20240111140918.1 |
| mtime |
20160408235959.9 |
| SCOPUS |
84973333072 |
| WOS |
000388373404094 |
| DOI |
10.1109/ICASSP.2016.7472493 |
| title
(primary) (eng) |
Blind separation of underdetermined linear mixtures based on source nonstationarity and AR(1) modeling |
| specification |
| page_count |
5 s. |
| media_type |
C |
|
| serial |
| ARLID |
cav_un_epca*0458486 |
| ISBN |
978-1-4799-9987-3 |
| title
|
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Proocessing |
| page_num |
4323-4327 |
| publisher |
| place |
Piscataway |
| name |
IEEE |
| year |
2016 |
|
|
| keyword |
Autoregressive Processes |
| keyword |
Cramer-Rao Bound |
| keyword |
Blind Source Separation |
| author
(primary) |
| ARLID |
cav_un_auth*0319418 |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
SI |
| full_dept |
Department of Stochastic Informatics |
| share |
40 |
| name1 |
Šembera |
| name2 |
Ondřej |
| institution |
UTIA-B |
| garant |
K |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101212 |
| full_dept (cz) |
Stochastická informatika |
| full_dept |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department |
SI |
| full_dept |
Department of Stochastic Informatics |
| share |
40 |
| name1 |
Tichavský |
| name2 |
Petr |
| institution |
UTIA-B |
| garant |
A |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0230113 |
| share |
20 |
| name1 |
Koldovský |
| name2 |
Z. |
| country |
CZ |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0303443 |
| project_id |
GA14-13713S |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
The problem of blind separation of underdetermined instantaneous mixtures of independent signals is addressed through a method relying on nonstationarity of the original signals. The signals are assumed to be piecewise stationary with varying variances in different epochs. In comparison with previous works, in this paper it is assumed that the signals are not i.i.d. in each epoch, but obey a first-order autoregressive model. This model was shown to be more appropriate for blind separation of natural speech signals. A separation method is proposed that is nearly statistically efficient (approaching the corresponding Cram´er-Rao lower bound), if the separated signals obey the assumed model. In the case of natural speech signals, the method is shown to have separation accuracy better than the state-of-the-art methods. |
| action |
| ARLID |
cav_un_auth*0329851 |
| name |
IEEE International Conference on Acoustics, Speech, and Signal Processing 2016 (ICASSP2016) |
| dates |
20.03.2016-25.03.2016 |
| place |
Shanghai |
| country |
CN |
|
| RIV |
BB |
| reportyear |
2017 |
| num_of_auth |
3 |
| presentation_type |
PO |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0259447 |
| confidential |
S |
| mrcbC86 |
3+4 Proceedings Paper Acoustics|Engineering Electrical Electronic |
| arlyear |
2016 |
| mrcbU14 |
84973333072 SCOPUS |
| mrcbU34 |
000388373404094 WOS |
| mrcbU56 |
305 kB |
| mrcbU63 |
cav_un_epca*0458486 Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Proocessing 978-1-4799-9987-3 4323 4327 Piscataway IEEE 2016 |
|