Signal Analysis Using Local Polynomial Approximations

Wildhaber, Reto; Ren, Elizabeth; Waldmann, Frédéric; Loeliger, Hans-Andrea (24 August 2020). Signal Analysis Using Local Polynomial Approximations 28th European Signal Processing Conference (EUSIPCO) Aug 2020.

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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.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	School of Engineering and Computer Science > Institute for Human Centered Engineering (HUCE) School of Engineering and Computer Science > Institute for Human Centered Engineering (HUCE) > HUCE / Laboratory for Microelectronics and Medical Devices School of Engineering and Computer Science
Name:	Wildhaber, Reto0000-0002-0849-0775; Ren, Elizabeth; Waldmann, Frédéric and Loeliger, Hans-Andrea
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Language:	English
Submitter:	Reto Wildhaber
Date Deposited:	09 Nov 2020 11:46
Last Modified:	14 Jan 2021 16:44
Uncontrolled Keywords:	localized polynomials, localized feature space, delay estimation, time-scale estimation, local signal approximation, autonomous linear state space models
ARBOR DOI:	10.24451/arbor.13181
URI:	https://arbor.bfh.ch/id/eprint/13181