Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
Enhanced Empirical Mode Decomposition using a Novel Sifting-Based Interpolation Points Detection
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
A flexible method for envelope estimation in empirical mode decomposition
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
IEEE Transactions on Signal Processing
One or Two Frequencies? The Empirical Mode Decomposition Answers
IEEE Transactions on Signal Processing
Development of EMD-based denoising methods inspired by wavelet thresholding
IEEE Transactions on Signal Processing
Instantaneous frequency based spectral analysis of nuclear magnetic resonance spectroscopy data
Computers and Electrical Engineering
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Empirical mode decomposition (EMD) is a signal analysis method which has received much attention lately due to its application in a number of fields. The main disadvantage of EMD is that it lacks a theoretical analysis and, therefore, our understanding of EMD comes from an intuitive and experimental validation of the method. Recent research on EMD revealed improved criteria for the interpolation points selection. More specifically, it was shown that the performance of EMD can be significantly enhanced if, as interpolation points, instead of the signal extrema, the extrema of the subsignal having the higher instantaneous frequency are used. Even if the extrema of the subsignal with the higher instantaneous frequency are not known in advance, this new interpolation points criterion can be effectively exploited in doubly-iterative sifting schemes leading to improved decomposition performance. In this paper, the possibilities and limitations of the developments above are explored and the new methods are compared with the conventional EMD.