0447196 20240111140907.220150925235959.9 000363785500035 84944711190 10.1007/978-3-319-22482-4_35 8 s. C cav_un_epca*0447195978-3-319-22482-40302-9743304-311HeidelbergSpringer2015VincentEmmanuelYeredorArieKoldovskýZbyněkTichavskýPetr Autoregressive processes Cramer-Rao bound Blind source separation cav_un_auth*0101212 Tichavský Petr Stochastická informatika Department of Stochastic Informatics SI SI UTIA-B Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0319418 Šembera Ondřej Stochastická informatika Department of Stochastic Informatics SI SI UTIA-B Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0108100 Koldovský Zbyněk Stochastická informatika Department of Stochastic Informatics SI SI UTIA-B Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2015/SI/tichavsky-0447196.pdf 214 kB GA14-13713S GA ČR CZ cav_un_auth*0303443 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. cav_un_auth*0319419 Latent Variable Analysis and Signal Separation 12th International Conference, LVA/ICA 2015 Liberec 25.08.2015-28.08.2015 CZ 2016 BI 3 PO RVO:67985556 http://hdl.handle.net/11104/0249577 S 0.329 Q2 Q2 2015 84944711190 SCOPUS 000363785500035 WOS 214 kB 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 Vincent Emmanuel 340 Yeredor Arie 340 Koldovský Zbyněk 340 Tichavský Petr 340