Blind Extraction of Moving Sources via Independent Component and Vector Analysis: Examples

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F21%3A00008778" target="_blank" >RIV/46747885:24220/21:00008778 - isvavai.cz</a>

Výsledek na webu

<a href="https://asap.ite.tul.cz/wp-content/uploads/sites/3/2021/03/ICASSP_2021___Examples_of_Blind_Source_Extraction_of_Moving_Sources.pdf" target="_blank" >https://asap.ite.tul.cz/wp-content/uploads/sites/3/2021/03/ICASSP_2021___Examples_of_Blind_Source_Extraction_of_Moving_Sources.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICASSP39728.2021.9413422" target="_blank" >10.1109/ICASSP39728.2021.9413422</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Blind Extraction of Moving Sources via Independent Component and Vector Analysis: Examples

Popis výsledku v původním jazyce

This paper is devoted to the recently proposed mixing model with constant separating vector (CSV) for Blind Source Extraction of moving sources using the FastDIVA algorithm, which is an extension of the famous FastICA and FastIVA for static mixtures. The benefits due to the CSV model and FastDIVA are demonstrated in three new applications. First, the extraction of a moving speaker in a noisy reverberant environment using a dense array of 48 MEMS microphones is considered. Second, a case study on the blind extraction of moving brain activity from visually evoked potentials in electroencephalogram is reported. Third, a simulation of block-by-block online extraction of a moving source is demonstrated. In these examples, the CSV and FastDIVA show their new potential and good performance in handling the blind moving source extraction problem.

Název v anglickém jazyce

Blind Extraction of Moving Sources via Independent Component and Vector Analysis: Examples

Popis výsledku anglicky

This paper is devoted to the recently proposed mixing model with constant separating vector (CSV) for Blind Source Extraction of moving sources using the FastDIVA algorithm, which is an extension of the famous FastICA and FastIVA for static mixtures. The benefits due to the CSV model and FastDIVA are demonstrated in three new applications. First, the extraction of a moving speaker in a noisy reverberant environment using a dense array of 48 MEMS microphones is considered. Second, a case study on the blind extraction of moving brain activity from visually evoked potentials in electroencephalogram is reported. Third, a simulation of block-by-block online extraction of a moving source is demonstrated. In these examples, the CSV and FastDIVA show their new potential and good performance in handling the blind moving source extraction problem.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA20-17720S" target="_blank" >GA20-17720S: Pokročilé modely směsí pro slepou extrakci signálů</a><br>

Návaznosti

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

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ISBN

—

ISSN

1520-6149

e-ISSN

—

Počet stran výsledku

5

Strana od-do

3725-3729

Název nakladatele

IEEE

Místo vydání

USA

Místo konání akce

Toronto, Canada

Datum konání akce

1. 1. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000704288403196