Independent Vector Extraction Constrained on Manifold of Half-Length Filters

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Independent Vector Extraction Constrained on Manifold of Half-Length Filters
Original language description
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.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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