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

Result code in IS VaVaI

RIV/67985556:_____/17:00472594 - isvavai.cz

Result on the web

http://dx.doi.org/10.1007/978-3-319-53547-0

DOI - Digital Object Identifier

10.1007/978-3-319-53547-0

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10103 - Statistics and probability

Project

GA17-00902S: Advanded Joint Blind Source Separation Methods

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Latent Variable Analysis and Signal Separation, 13th International Conference, LVA/ICA 2017

ISBN

978-3-319-53546-3

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

11

Pages from-to

36-46

Publisher name

Springer

Place of publication

Cham

Event location

Grenoble

Event date

Feb 21, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000418581400004