bibtype C - Conference Paper (international conference)
ARLID 0447196
utime 20240111140907.2
mtime 20150925235959.9
WOS 000363785500035
SCOPUS 84944711190
DOI 10.1007/978-3-319-22482-4_35
title (primary) (eng) Blind Separation of Mixtures of Piecewise AR(1) Processes and Model Mismatch
specification
page_count 8 s.
media_type C
serial
ARLID cav_un_epca*0447195
ISBN 978-3-319-22482-4
ISSN 0302-9743
title Latent Variable Analysis and Signal Separation
page_num 304-311
publisher
place Heidelberg
name Springer
year 2015
editor
name1 Vincent
name2 Emmanuel
editor
name1 Yeredor
name2 Arie
editor
name1 Koldovský
name2 Zbyněk
editor
name1 Tichavský
name2 Petr
keyword Autoregressive processes
keyword Cramer-Rao bound
keyword Blind source separation
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*0319418
name1 Šembera
name2 Ondřej
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*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
url http://library.utia.cas.cz/separaty/2015/SI/tichavsky-0447196.pdf
source_size 214 kB
cas_special
project
project_id GA14-13713S
agency GA ČR
country CZ
ARLID cav_un_auth*0303443
abstract (eng) Modeling real-world acoustic signals and namely speech signals as piecewise stationary random processes is a possible approach to blind separation of linear mixtures of such signals. In this paper, the piecewise AR(1) modeling is studied and is compared to the more common piecewise AR(0) modeling, which is known under the names Block Gaussian SEParation (BGSEP) and Block Gaussian Likelihood (BGL). The separation based on the AR(0) modeling uses an approximate joint diagonalization (AJD) of covariance matrices of the mixture with lag 0, computed at epochs (intervals) of stationarity of the separated signals. The separation based on the AR(1) modeling uses the covariances of lag 0 and covariances of lag 1 jointly. For this model, we derive an approximate Cram´er-Rao lower bound on the separation accuracy for estimation based on the full set of the statistics (covariance matrices of lag 0 and lag 1) and covariance matrices with lag 0 only. The bounds show the condition when AR(1) modeling leads to significantly improved separation accuracy.
action
ARLID cav_un_auth*0319419
name Latent Variable Analysis and Signal Separation 12th International Conference, LVA/ICA 2015
place Liberec
dates 25.08.2015-28.08.2015
country CZ
reportyear 2016
RIV BI
num_of_auth 3
presentation_type PO
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0249577
confidential S
mrcbT16-s 0.329
mrcbT16-4 Q2
mrcbT16-E Q2
arlyear 2015
mrcbU14 84944711190 SCOPUS
mrcbU34 000363785500035 WOS
mrcbU56 214 kB
mrcbU63 cav_un_epca*0447195 Latent Variable Analysis and Signal Separation 978-3-319-22482-4 0302-9743 304 311 Heidelberg Springer 2015 LNCS 9237 Lecture Notes in Computer Science
mrcbU67 Vincent Emmanuel 340
mrcbU67 Yeredor Arie 340
mrcbU67 Koldovský Zbyněk 340
mrcbU67 Tichavský Petr 340