0458485 20240111140918.120160408235959.9 84973333072 000388373404094 10.1109/ICASSP.2016.7472493 5 s. C cav_un_epca*0458486978-1-4799-9987-34323-4327PiscatawayIEEE2016 Autoregressive Processes Cramer-Rao Bound Blind Source Separation cav_un_auth*0319418 Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics 40 Šembera Ondřej UTIA-B K Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101212 Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics 40 Tichavský Petr UTIA-B A Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0230113 20 Koldovský Z. CZ http://library.utia.cas.cz/separaty/2016/SI/tichavsky-0458485.pdf 305 kB cav_un_auth*0303443 GA14-13713S GA ČR CZ The problem of blind separation of underdetermined instantaneous mixtures of independent signals is addressed through a method relying on nonstationarity of the original signals. The signals are assumed to be piecewise stationary with varying variances in different epochs. In comparison with previous works, in this paper it is assumed that the signals are not i.i.d. in each epoch, but obey a first-order autoregressive model. This model was shown to be more appropriate for blind separation of natural speech signals. A separation method is proposed that is nearly statistically efficient (approaching the corresponding Cram´er-Rao lower bound), if the separated signals obey the assumed model. In the case of natural speech signals, the method is shown to have separation accuracy better than the state-of-the-art methods. cav_un_auth*0329851 IEEE International Conference on Acoustics, Speech, and Signal Processing 2016 (ICASSP2016) 20.03.2016-25.03.2016 Shanghai CN BB 2017 3 PO RVO:67985556 http://hdl.handle.net/11104/0259447 S 3+4 Proceedings Paper Acoustics|Engineering Electrical Electronic 2016 84973333072 SCOPUS 000388373404094 WOS 305 kB cav_un_epca*0458486 Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Proocessing 978-1-4799-9987-3 4323 4327 Piscataway IEEE 2016