Orthogonally constrained independent component extraction: Blind MPDR beamforming

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00478273" target="_blank" >RIV/67985556:_____/17:00478273 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.23919/EUSIPCO.2017.8081389" target="_blank" >http://dx.doi.org/10.23919/EUSIPCO.2017.8081389</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/EUSIPCO.2017.8081389" target="_blank" >10.23919/EUSIPCO.2017.8081389</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Orthogonally constrained independent component extraction: Blind MPDR beamforming

Popis výsledku v původním jazyce

We propose a novel technique for the extraction of one independent component from an instantaneous linear complex-valued mixture of signals. The mixing model is optimizednin terms of the number of parameters that are necessary to simultaneously estimate one column of the mixing matrix and one row of the de-mixing matrix, which both correspond to the desired source. The desired source is assumed to have a non-Gaussian distribution, while the other sources are modeled, for simplicity, as Gaussian-distributed, although in applications the other sources can be arbitrary. We propose an algorithm that can be interpreted as a blind self-steering Minimum-Power Distortionless Response (MPDR) beamformer. The method is compared with the popular Natural Gradient algorithm for general Independent Component Analysis. Their performances arencomparable but the proposed method has a lower computational complexity, in examples, it is about four times faster.

Název v anglickém jazyce

Orthogonally constrained independent component extraction: Blind MPDR beamforming

Popis výsledku anglicky

We propose a novel technique for the extraction of one independent component from an instantaneous linear complex-valued mixture of signals. The mixing model is optimizednin terms of the number of parameters that are necessary to simultaneously estimate one column of the mixing matrix and one row of the de-mixing matrix, which both correspond to the desired source. The desired source is assumed to have a non-Gaussian distribution, while the other sources are modeled, for simplicity, as Gaussian-distributed, although in applications the other sources can be arbitrary. We propose an algorithm that can be interpreted as a blind self-steering Minimum-Power Distortionless Response (MPDR) beamformer. The method is compared with the popular Natural Gradient algorithm for general Independent Component Analysis. Their performances arencomparable but the proposed method has a lower computational complexity, in examples, it is about four times faster.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA17-00902S" target="_blank" >GA17-00902S: Pokročilé metody slepé separace podprostorů</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 25th European Signal Processing Conference (EUSIPCO 2017)

ISBN

978-0-9928626-7-1

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1195-1199

Název nakladatele

EURASIP

Místo vydání

Atheny

Místo konání akce

Kos

Datum konání akce

28. 8. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000426986000233