Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Improved EMD using doubly-iterative sifting and high order spline interpolation
EURASIP Journal on Advances in Signal Processing
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
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
Single-Mixture Audio Source Separation by Subspace Decomposition of Hilbert Spectrum
IEEE Transactions on Audio, Speech, and Language Processing
Robust Image Watermarking Based on Multiband Wavelets and Empirical Mode Decomposition
IEEE Transactions on Image Processing
Iterative Filtering Decomposition Based on Local Spectral Evolution Kernel
Journal of Scientific Computing
Mode Decomposition Evolution Equations
Journal of Scientific Computing
Empirical mode decomposition on surfaces
Graphical Models
Simplified noise model parameter estimation for signal-dependent noise
Signal Processing
Hi-index | 35.68 |
One of the tasks for which empirical mode decomposition (EMD) is potentially useful is nonparametric signal denoising, an area for which wavelet thresholding has been the dominant technique for many years. In this paper, the wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal. We show that although a direct application of this principle is not feasible in the EMD case, it can be appropriately adapted by exploiting the special characteristics of the EMD decomposition modes. In the same manner, inspired by the translation invariant wavelet thresholding, a similar technique adapted to EMD is developed, leading to enhanced denoising performance.