| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0348108 |
| utime |
20240111140745.2 |
| mtime |
20101102235959.9 |
| title
(primary) (eng) |
Adaptive Time-Domain Blind Separation of Speech Signals |
| specification |
| page_count |
8 s. |
| media_type |
memory stick |
|
| serial |
| ARLID |
cav_un_epca*0348107 |
| ISBN |
978-3-642-15994-7 |
| title
|
Lecture Notes in Computer Science |
| part_num |
6365 |
| page_num |
9-16 |
| publisher |
| place |
Heidelberg |
| name |
Springer-Verlag |
| year |
2010 |
|
| editor |
| name1 |
Gavrilova |
| name2 |
M.L. |
|
| editor |
|
| editor |
|
| editor |
|
| editor |
|
|
| keyword |
blind source separation |
| keyword |
speech |
| keyword |
convolutive mixture |
| keyword |
adaptive algorithms |
| author
(primary) |
| ARLID |
cav_un_auth*0050739 |
| name1 |
Málek |
| name2 |
J. |
| country |
CZ |
|
| 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. |
|
| author
|
| ARLID |
cav_un_auth*0101212 |
| name1 |
Tichavský |
| name2 |
Petr |
| 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 |
|
| project |
| project_id |
GA102/08/0707 |
| agency |
GA ČR |
| country |
CZ |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
We present an adaptive algorithm for blind audio source separation (BASS) of moving sources via Independent Component Analysis (ICA) in time-domain. The method is shown to achieve good separation quality even with a short demixing filter length (L = 30). Our experiments show that the proposed adaptive algorithm can outperform the off-line version of the method (in terms of the average output SIR), even in the case in which the sources do not move, because it is capable of better adaptation to the nonstationarity of the speech. |
| action |
| ARLID |
cav_un_auth*0264720 |
| name |
Latent Variable Analysis and Signal Separation |
| place |
St. Malo |
| dates |
27.09.2010-30.09.2010 |
| country |
FR |
|
| reportyear |
2011 |
| RIV |
BB |
| permalink |
http://hdl.handle.net/11104/0188720 |
| arlyear |
2010 |
| mrcbU56 |
281kB |
| mrcbU63 |
cav_un_epca*0348107 Lecture Notes in Computer Science 6365 978-3-642-15994-7 9 16 Heidelberg Springer-Verlag 2010 Latent Variable Analysis and Signal Separation, 9th Int. Conf., LVA/ICA 2010 |
| mrcbU67 |
Gavrilova M.L. 340 |
| mrcbU67 |
Kumar V. 340 |
| mrcbU67 |
Mun Y. 340 |
| mrcbU67 |
Tan C.J.K. 340 |
| mrcbU67 |
Gervasi O. 340 |
|