Institute of Information Theory and Automation

You are here

Bibliography

Conference Paper (international conference)

Blind Source Separation of Single Channel Mixture Using Tensorization and Tensor Diagonalization

Phan A. H., Tichavský Petr, Cichocki A.

: Latent Variable Analysis and Signal Separation, 13th International Conference, LVA/ICA 2017, p. 36-46 , Eds: Tichavský Petr, Babaie-Zadeh Massoud, Michel Olivier J.J., Thirion-Moreau Nadege

: Latent Variable Analysis and Signal Separation, (Grenoble, FR, 20170221)

: GA17-00902S, GA ČR

: blind source separation, tensor diagonalization, block-term decomposition, damped sinusoid

: 10.1007/978-3-319-53547-0

: http://library.utia.cas.cz/separaty/2017/SI/tichavsky-0472594.pdf

(eng): 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.

: BB

: 10103

2019-01-07 08:39