Signal Analysis Using Local Polynomial Approximations
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Author / Producer
Date
2021
Publication Type
Conference Paper
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yes
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Abstract
Local polynomial approximations represent a versatile feature space for time-domain signal analysis. The parameters of such polynomial approximations can be computed by efficient recursions using autonomous linear state space models and often allow analytical solutions for quantities of interest. The approach is illustrated by practical examples including the estimation of the delay difference between two acoustic signals and template matching in electrocardiogram signals with local variations in amplitude and time scale. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.
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published
Editor
Book title
2020 28th European Signal Processing Conference (EUSIPCO)
Journal / series
Volume
Pages / Article No.
2239 - 2243
Publisher
IEEE
Event
28th European Signal Processing Conference (EUSIPCO 2020) (virtual)
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Methods
Software
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Date created
Subject
localized polynomials; localized feature space; delay estimation; time-scale estimation; local signal approximation; autonomous linear state space models
Organisational unit
03568 - Loeliger, Hans-Andrea / Loeliger, Hans-Andrea
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.