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Original title:
Blind Separation of Mixtures of Piecewise AR(1) Processes and Model Mismatch
Authors: Tichavský, Petr ; Šembera, Ondřej ; Koldovský, Zbyněk
Document type: Papers
Conference/Event: Latent Variable Analysis and Signal Separation 12th International Conference, LVA/ICA 2015, Liberec (CZ), 2015-08-25 / 2015-08-28
Year: 2015
Language: eng
Abstract: 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.
Keywords: Autoregressive processes; Blind source separation; Cramer-Rao bound
Project no.: GA14-13713S (CEP)
Funding provider: GA ČR
Host item entry: Latent Variable Analysis and Signal Separation, ISBN 978-3-319-22482-4, ISSN 0302-9743
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2015/SI/tichavsky-0447196.pdf
Original record: http://hdl.handle.net/11104/0249577
Permalink: http://www.nusl.cz/ntk/nusl-200415
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Papers
Authors: Tichavský, Petr ; Šembera, Ondřej ; Koldovský, Zbyněk
Document type: Papers
Conference/Event: Latent Variable Analysis and Signal Separation 12th International Conference, LVA/ICA 2015, Liberec (CZ), 2015-08-25 / 2015-08-28
Year: 2015
Language: eng
Abstract: 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.
Keywords: Autoregressive processes; Blind source separation; Cramer-Rao bound
Project no.: GA14-13713S (CEP)
Funding provider: GA ČR
Host item entry: Latent Variable Analysis and Signal Separation, ISBN 978-3-319-22482-4, ISSN 0302-9743
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2015/SI/tichavsky-0447196.pdf
Original record: http://hdl.handle.net/11104/0249577
Permalink: http://www.nusl.cz/ntk/nusl-200415
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Papers
Record created 2015-09-25, last modified 2022-09-29