Independent Vector Extraction Constrained on Manifold of Half-Length Filters

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F23%3A00011012" target="_blank" >RIV/46747885:24220/23:00011012 - isvavai.cz</a>

Výsledek na webu

<a href="https://arxiv.org/abs/2304.01778" target="_blank" >https://arxiv.org/abs/2304.01778</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/EUSIPCO58844.2023.10289896" target="_blank" >10.23919/EUSIPCO58844.2023.10289896</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Independent Vector Extraction Constrained on Manifold of Half-Length Filters

Popis výsledku v původním jazyce

Independent Vector Analysis (IVA) is a popular extension of Independent Component Analysis (ICA) for joint separation of a set of instantaneous linear mixtures, with a direct application in frequency-domain speaker separation or extraction. The mixtures are parameterized by mixing matrices, one matrix per mixture. This means that the IVA mixing model does not account for any relationships between parameters across the mixtures/frequencies. The separation proceeds jointly only through the source model, where statistical dependencies of sources across the mixtures are taken into account. In this paper, we propose a mixing model for joint blind source extraction where the mixing model parameters are linked across the frequencies. This is achieved by constraining the set of feasible parameters to the manifold of half-length separating filters, which has a clear interpretation and application in frequency-domain speaker extraction.

Název v anglickém jazyce

Independent Vector Extraction Constrained on Manifold of Half-Length Filters

Popis výsledku anglicky

Independent Vector Analysis (IVA) is a popular extension of Independent Component Analysis (ICA) for joint separation of a set of instantaneous linear mixtures, with a direct application in frequency-domain speaker separation or extraction. The mixtures are parameterized by mixing matrices, one matrix per mixture. This means that the IVA mixing model does not account for any relationships between parameters across the mixtures/frequencies. The separation proceeds jointly only through the source model, where statistical dependencies of sources across the mixtures are taken into account. In this paper, we propose a mixing model for joint blind source extraction where the mixing model parameters are linked across the frequencies. This is achieved by constraining the set of feasible parameters to the manifold of half-length separating filters, which has a clear interpretation and application in frequency-domain speaker extraction.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10102 - Applied mathematics

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

European Signal Processing Conference

ISBN

978-946459360-0

ISSN

22195491

e-ISSN

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Počet stran výsledku

5

Strana od-do

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Název nakladatele

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Místo vydání

—

Místo konání akce

Helsinky

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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