Empirical mode decomposition as a filter bank

By Patrick Flandrin, Gabriel Rilling, Paulo Gonçalves. IEEE Signal Processing Letters, 2004

2,495 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/dfcd689be4d922ef599ddc1a378b7e9398c12e24

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Citations in the corpus

Generator Coherency Using the Hilbert–Huang Transform (2008, methodology): “The technique has been modified and improved to enhance its ability to discriminate between closely spaced frequency components [9]–[11].”
ECG signal denoising and baseline wander correction based on the empirical mode decomposition (2008, background): “It is reported in [17] that EMD behaves as a “wavelet-like” dyadic filter bank for fractional Gaussian noise.”
Trend Filtering Methods for Momentum Strategies (2011, background): “The most recent include, for example, singular spectrum analysis24 (Vautard et al., 1992), support vector machines25 and empirical mode decomposition (Flandrin et al., 2004).”
The Hilbert-Huang transform: Theory, applications, development (2011, methodology): “components which differ in period by more than factors of two [15][57].”
The modulated annual cycle: an alternative reference frame for climate anomalies (2007, methodology): “It behaves as a dyadic filter bank2 in which the Fourier spectra of various IMFs collapse to a single shape along the axis of logarithm of period or frequency (Flandrin et al. 2004; Wu and Huang 2004, 2005a).”
One or Two Frequencies? The Empirical Mode Decomposition Answers (2008, background): “Possible extensions to stochastic and nonlinear situations are brieﬂy mentioned in the Conclusion.”
Filter Bank Property of Multivariate Empirical Mode Decomposition (2011, methodology): “Corresponding analyses for standard EMD revealed that IMFs tend to mimic a filter bank structure, similar to that observed in the case of wavelet decompositions [10], [11].”
Analysis of daily river flow fluctuations using Empirical Mode Decomposition and arbitrary order Hilbert spectral analysis (2009, methodology): “It has been shown to be an efficient method to separate151 a signal into a trend and small scale fluctuations on a dyadic bank (Wu152 and Huang, 2004; Flandrin et al., 2004; Flandrin and Gonçalvès, 2004); it153 has also been applied to many fields including physiology (Su et al., 2008),154 geophys”
Multivariate Variational Mode Decomposition (2019, methodology): “In [27], [35], EMD and MEMD had respectively been shown to exhibit quasi-dyadic filterbank structure for wGn, which is similar to the wavelet filterbank.”
A complete ensemble empirical mode decomposition with adaptive noise (2011, background): “The addition of white Gaussian noise solves the mode mixing problem by populating the whole time-frequency space to take advantage of the dyadic filter bank behavior of the EMD [3]; however it creates some new ones.”
Detrending and denoising with empirical mode decompositions (2004, methodology): “The technique has already received some attention in terms of both applications [1, 2, 7, 9, 11] and interpretation [3, 4, 5, 8].”
Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding (2009, background): “Although many attempts have been made to improve the understanding of the way EMD operates or to enhance its performance (see, for example, [5]–[9]), EMD still lacks a sound mathematical theory and is essentially described by an algorithm.”
On empirical mode decomposition and its algorithms (2003, result): “This supports an interpretation of EMD in terms of a constant-Q filter bank, in close agreement with the findings reported in [1, 4, 7] in stochastic situations involving broadband noise.”