Blind separation of underdetermined linear mixtures based on source nonstationarity and AR(1) modeling
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00458485" target="_blank" >RIV/67985556:_____/16:00458485 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Blind separation of underdetermined linear mixtures based on source nonstationarity and AR(1) modeling
Original language description
The problem of blind separation of underdetermined instantaneous mixtures of independent signals is addressed through a method relying on nonstationarity of the original signals. The signals are assumed to be piecewise stationary with varying variances in different epochs. In comparison with previous works, in this paper it is assumed that the signals are not id. in each epoch, but obey a first-order autoregressive model. This model was shown to be more appropriate for blind separation of natural speech signals. A separation method is proposed that is nearly statistically efficient (approaching the corresponding Cram´er-Rao lower bound), if the separated signals obey the assumed model. In the case of natural speech signals, the method is shown to have separation accuracy better than the state-of-the-art methods.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA14-13713S" target="_blank" >GA14-13713S: Tensor Decomposition Methods and Their Applications</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Proocessing
ISBN
978-1-4799-9987-3
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
4323-4327
Publisher name
IEEE
Place of publication
Piscataway
Event location
Shanghai
Event date
Mar 20, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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