Algorithm for Independent Vector Extraction Based on Semi-Time-Variant Mixing Model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F21%3A00374187" target="_blank" >RIV/68407700:21340/21:00374187 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/46747885:24220/21:00008780

Výsledek na webu

<a href="https://doi.org/10.23919/EUSIPCO54536.2021.9616272" target="_blank" >https://doi.org/10.23919/EUSIPCO54536.2021.9616272</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/EUSIPCO54536.2021.9616272" target="_blank" >10.23919/EUSIPCO54536.2021.9616272</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Algorithm for Independent Vector Extraction Based on Semi-Time-Variant Mixing Model

Popis výsledku v původním jazyce

A new algorithm for dynamic independent vector extraction is proposed. It is based on the mixing model where mixing parameters related to the source-of-interest (SOI) are time-variant while the separating parameters are time-invariant. A contrast function based on the quasi-likelihood approach is optimized using the Newton-Raphson approach. The update is computed without imposing the orthogonal constraint, and the orthogonality is enforced afterward. This yields an algorithm that is significantly faster than gradient-based algorithms while different from fixed-point methods, which are even faster. We show advantageous properties of the proposed algorithm compared to the fixed-point methods in an on-line processing regime where stable convergence to the SOI is the important issue. The effectiveness of the method is demonstrated in a speech extraction experiment with a dense microphone array.

Název v anglickém jazyce

Algorithm for Independent Vector Extraction Based on Semi-Time-Variant Mixing Model

Popis výsledku anglicky

A new algorithm for dynamic independent vector extraction is proposed. It is based on the mixing model where mixing parameters related to the source-of-interest (SOI) are time-variant while the separating parameters are time-invariant. A contrast function based on the quasi-likelihood approach is optimized using the Newton-Raphson approach. The update is computed without imposing the orthogonal constraint, and the orthogonality is enforced afterward. This yields an algorithm that is significantly faster than gradient-based algorithms while different from fixed-point methods, which are even faster. We show advantageous properties of the proposed algorithm compared to the fixed-point methods in an on-line processing regime where stable convergence to the SOI is the important issue. The effectiveness of the method is demonstrated in a speech extraction experiment with a dense microphone array.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

29th European Signal Processing Conference (EUSIPCO)

ISBN

9789082797060

ISSN

2219-5491

e-ISSN

2076-1465

Počet stran výsledku

5

Strana od-do

865-869

Název nakladatele

IEEE Signal Processing Society

Místo vydání

New Jersey

Místo konání akce

Dublin (virtual)

Datum konání akce

23. 8. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000764066600172