EMD-Based Signal Filtering

2007/12/01 by Abdel-Ouahab Boudraa, Abdel‐Ouahab Boudraa, Jean-Christophe Cexus · 657 citations
Computer Science · Engineering · Mathematics · #Additive white Gaussian noise #Algorithm #Analog signal #Artificial intelligence #Basis (linear algebra) #Computer science #Computer vision #Digital signal processing #Energy (signal processing) #Filter (signal processing) #Gaussian #Gaussian noise #Hilbert–Huang transform #Image (mathematics) #Image and Signal Denoising Methods #Instantaneous phase #Machine Fault Diagnosis Techniques #Mathematics #Noise (video) #Noise measurement #Noise reduction #SIGNAL (programming language) #Signal processing #Signal transfer function #Statistics #Structural Health Monitoring Techniques #White noise

paper · doi:10.1109/tim.2007.907967

published in IEEE Transactions on Instrumentation and Measurement 56(6), 2196-2202 (Institute of Electrical and Electronics Engineers)

openalex publication_date 2007/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

In this paper, a signal-filtering method based on empirical mode decomposition is proposed. The filtering method is a fully data-driven approach. A noisy signal is adaptively decomposed into intrinsic oscillatory components called intrinsic mode functions (IMFs) by means of an algorithm referred to as a sifting process. The basic principle of the method is to make use of partial reconstructions of the signal, with the relevant IMFs corresponding to the most important structures of the signal (low-frequency components). A criterion is proposed to determine the IMF, after which, the energy distribution of the important structures of the signal overcomes that of the noise and that of the high-frequency components of the signal. The method is illustrated on simulated and real data, and the results are compared to well-known filtering methods. The study is limited to signals that were corrupted by additive white Gaussian noise and is conducted on the basis of extended numerical experiments.

Citations

• De-noising by soft-thresholding
• Ideal spatial adaptation by wavelet shrinkage

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