Blind Extraction of Moving Sources via Independent Component and Vector Analysis: Examples
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Blind Extraction of Moving Sources via Independent Component and Vector Analysis: Examples
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA20-17720S" target="_blank" >GA20-17720S: Advanced Mixing Models for Blind Source Extraction</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISBN
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ISSN
1520-6149
e-ISSN
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Number of pages
5
Pages from-to
3725-3729
Publisher name
IEEE
Place of publication
USA
Event location
Toronto, Canada
Event date
Jan 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000704288403196