Open AccessEmpirical Mode Decomposition in Discrete Time Signals Denoising
Author(s) -
Zoltán Germán-Salló
Publication year - 2019
Publication title -
acta marisiensis. seria technologica
Language(s) - English
Resource type - Journals
ISSN - 2668-4217
DOI - 10.2478/amset-2019-0002
Subject(s) - hilbert–huang transform , signal (programming language) , noise reduction , algorithm , signal processing , step detection , noise (video) , mathematics , mean squared error , hurst exponent , decomposition , computer science , pattern recognition (psychology) , artificial intelligence , digital signal processing , statistics , filter (signal processing) , white noise , ecology , image (mathematics) , computer vision , biology , computer hardware , programming language
This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the analyzed signal into components without using expansion functions. This is a signal dependent representation and provides intrinsic mode functions (IMFs) as components. These are analyzed, through their Hurst exponent and if they are found being noisy components they will be partially or integrally eliminated. This study presents an EMD decomposition-based filtering procedure applied to test signals, the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.