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KLEJMOVÁ, E. POMĚNKOVÁ, J. BLUMENSTEIN, J.
Originální název
Combination of Time-Frequency representations for Background Noise Suppresion
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
The aim of the paper is to propose approach for enhancement of time-frequency representation leading to the background noise suppression. The approach is based on combination of continuous wavelet analysis, time-varying autoregressive process and short time Fourier transform. By such combination we make the identification of important areas in the time-frequency representation easier. The proposed method is an alternative approach to significance tests which can be problematic in some cases. The performance of methods is presented on the gross domestic product of the United Kingdom and Group of 7. The results show that in the UK, oil crisis has a bigger impact compared to financial crisis, while from the perspectives of G7 countries, the impact of financial crisis was stronger. The obtained results can be also used for consequent econometric analysis which identify dependencies, relations, bilateral causalities or other economic aspects.
Klíčová slova
continuous wavelet transform, time-varying autoregressive process, short time Fourier transform, time-frequency representation, noise suppression
Autoři
KLEJMOVÁ, E.; POMĚNKOVÁ, J.; BLUMENSTEIN, J.
Vydáno
23. 5. 2017
Nakladatel
Digitaldruck Pirrot GmbH
Místo
Německo
ISBN
978-0-9932440-4-9
Kniha
Proceedings 31st European Conference on Moelling and Simulation ECMS 2017
Strany od
93
Strany do
99
Strany počet
7
URL
http://www.scs-europe.net/dlib/2017/ecms2017acceptedpapers/0093-fes_ECMS2017_0076.pdf
BibTex
@inproceedings{BUT134461, author="Eva {Klejmová} and Jitka {Dluhá} and Jiří {Blumenstein}", title="Combination of Time-Frequency representations for Background Noise Suppresion", booktitle="Proceedings 31st European Conference on Moelling and Simulation ECMS 2017", year="2017", pages="93--99", publisher="Digitaldruck Pirrot GmbH", address="Německo", isbn="978-0-9932440-4-9", url="http://www.scs-europe.net/dlib/2017/ecms2017acceptedpapers/0093-fes_ECMS2017_0076.pdf" }