0472594 20240111140936.320170313235959.9 85013448377 000418581400004 10.1007/978-3-319-53547-0 Blind Source Separation of Single Channel Mixture Using Tensorization and Tensor Diagonalization 11 s. C cav_un_epca*0472593978-3-319-53546-30302-9743Latent Variable Analysis and Signal Separation, 13th International Conference, LVA/ICA 201736-46ChamSpringer2017TichavskýPetrBabaie-ZadehMassoudMichelOlivier J.J.Thirion-MoreauNadege blind source separation tensor diagonalization block-term decomposition damped sinusoid cav_un_auth*0274170 Phan A. H. JP 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*0274171 Cichocki A. JP http://library.utia.cas.cz/separaty/2017/SI/tichavsky-0472594.pdf 299 kB cav_un_auth*0345929 GA17-00902S GA ČR This paper deals with estimation of structured signals such as damped sinusoids, exponentials, polynomials, and their products from single channel data. It is shown that building tensors from this kind of data results in tensors with hidden block structure which can be recovered through the tensor diagonalization. The tensor diagonalization means multiplying tensors by several matrices along its modes so that the outcome is approximately diagonal or block-diagonal of 3-rd order tensors. The proposed method can be applied to estimation of parameters of multiple damped sinusoids, and their products with polynomial. cav_un_auth*0344250 Latent Variable Analysis and Signal Separation 20170221 20170223 Grenoble FR BB 10000 10100 10103 2018 3 PR RVO:67985556 http://hdl.handle.net/11104/0271357 S n.a. Proceedings Paper Acoustics|Computer Science Theory Methods n.a. Proceedings Paper Acoustics|Computer Science Theory Methods n.a. Proceedings Paper Acoustics|Computer Science Theory Methods 0.328 Q2 Q2 2017 85013448377 SCOPUS PUBMED 000418581400004 WOS 299 kB cav_un_epca*0472593 Latent Variable Analysis and Signal Separation, 13th International Conference, LVA/ICA 2017 978-3-319-53546-3 0302-9743 1611-3349 36 46 Cham Springer 2017 Lecture Notes in Computer Science 10169 340 Tichavský Petr 340 Babaie-Zadeh Massoud 340 Michel Olivier J.J. 340 Thirion-Moreau Nadege