0472594 - ÚTIA 2018 RIV CH eng C - Konferenční příspěvek (zahraniční konf.)
Phan, A. H. - Tichavský, Petr - Cichocki, A.
Blind Source Separation of Single Channel Mixture Using Tensorization and Tensor Diagonalization.
Latent Variable Analysis and Signal Separation, 13th International Conference, LVA/ICA 2017. Cham: Springer, 2017 - (Tichavský, P.; Babaie-Zadeh, M.; Michel, O.; Thirion-Moreau, N.), s. 36-46. Lecture Notes in Computer Science, 10169. ISBN 978-3-319-53546-3. ISSN 0302-9743. E-ISSN 1611-3349.
[Latent Variable Analysis and Signal Separation. Grenoble (FR), 21.02.2017-23.02.2017]
Grant CEP: GA ČR GA17-00902S
Institucionální podpora: RVO:67985556
Klíčová slova: blind source separation * tensor diagonalization * block-term decomposition * damped sinusoid
Obor OECD: Statistics and probability
Web výsledku:
http://library.utia.cas.cz/separaty/2017/SI/tichavsky-0472594.pdf
DOI: https://doi.org/10.1007/978-3-319-53547-0

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.
Trvalý link: http://hdl.handle.net/11104/0271357