Supervised Independent Vector Analysis Through Pilot Dependent Components
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F17%3A00004532" target="_blank" >RIV/46747885:24220/17:00004532 - isvavai.cz</a>
Result on the web
<a href="https://asap.ite.tul.cz/wp-content/uploads/sites/3/2017/03/icassp2017.pdf" target="_blank" >https://asap.ite.tul.cz/wp-content/uploads/sites/3/2017/03/icassp2017.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSP.2017.7952213" target="_blank" >10.1109/ICASSP.2017.7952213</a>
Alternative languages
Result language
angličtina
Original language name
Supervised Independent Vector Analysis Through Pilot Dependent Components
Original language description
Unknown global permutation of the separated sources, time-varying source activity and under determination are common problems affecting on-line Independent Vector Analysis when applied to real-world speech enhancement. In this work we propose to extend the signal model of IVA by introducing additional supervising components. Pilot signals, which are dependent on the sources, are injected in the multidimensional source representation and act as a prior knowledge. The resulting adaptation still maximizes the multivariate source independence, while simultaneously forcing the estimation of sources dependent on the pilot components. It is also shown as the S-IVA is a generalization over the previously proposed weighted Natural Gradient. Numerical evaluations shows the effectiveness of the proposed method in challenging real-world applications.
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
—
Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IEEE International Conference on Audio, Speech, and Signal Processing 2017
ISBN
978-1-5090-4117-6
ISSN
15206149
e-ISSN
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Number of pages
5
Pages from-to
536-540
Publisher name
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Place of publication
New Orleans, USA
Event location
New Orleans, USA
Event date
Jan 1, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000414286200108