Improved EMD using doubly-iterative sifting and high order spline interpolation
EURASIP Journal on Advances in Signal Processing
Energy separation in signal modulations with application to speechanalysis
IEEE Transactions on Signal Processing
Comments on “Sinc interpolation of discrete periodicsignals”
IEEE Transactions on Signal Processing
One or Two Frequencies? The Empirical Mode Decomposition Answers
IEEE Transactions on Signal Processing
Sinc interpolation of discrete periodic signals
IEEE Transactions on Signal Processing
Multicomponent AM–FM Representations: An Asymptotically Exact Approach
IEEE Transactions on Audio, Speech, and Language Processing
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Empirical mode decomposition (EMD) is a new data analysis method which depends on the exact location of the extrema in the signal. Therefore, the available EMD algorithm only works under high sampling rate in order to find the extrema exactly. Fourier interpolation has been used to solve the problem. This paper discusses the error expression of Fourier interpolation and derives its error upper bound for general band-limited signals, which implies Fourier interpolation yields errors especially near the boundary when the signal is non-integer-period sampled. Motivated by this, a hybrid extrema estimation algorithm based on Fourier interpolation is proposed. Simulation results show that the hybrid algorithm is efficient in improving the performance of the EMD under low sampling rate.