Blind Separation of Mixtures of Piecewise AR(1) Processes and Model Mismatch
Result description
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 comparedto 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 approximateCram´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
Keywords
Autoregressive processesCramer-Rao boundBlind source separation
The result's identifiers
Result code in IS VaVaI
Result on the web
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
Blind Separation of Mixtures of Piecewise AR(1) Processes and Model Mismatch
Original language description
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 comparedto 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 approximateCram´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
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BI - Acoustics and oscillation
OECD FORD branch
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Result continuities
Project
GA14-13713S: Tensor Decomposition Methods and Their Applications
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Latent Variable Analysis and Signal Separation
ISBN
978-3-319-22482-4
ISSN
0302-9743
e-ISSN
—
Number of pages
8
Pages from-to
304-311
Publisher name
Springer
Place of publication
Heidelberg
Event location
Liberec
Event date
Aug 25, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000363785500035
Basic information
Result type
D - Article in proceedings
CEP
BI - Acoustics and oscillation
Year of implementation
2015