Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
Nonlinear time series analysis
Nonlinear time series analysis
Signal Period Analysis Based on Hilbert-Huang Transform and Its Application to Texture Analysis
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
An EMD-based recognition method for Chinese fonts and styles
Pattern Recognition Letters
A novel pitch period detection algorithm based on hilbert-huang transform
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
IEEE Transactions on Signal Processing
One or Two Frequencies? The Empirical Mode Decomposition Answers
IEEE Transactions on Signal Processing
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It has been found that envelopes established by extrema in the empirical mode decomposition cannot always depict the local characteristics of a signal very well. This is due in part to the slight oscillations characterized as hidden scales which are almost left untreated during the sifting process. When involving hidden scales, the intrinsic mode function usually contains at a given instance multiple oscillation modes. In view of this, based on inflection points this paper presents a new decomposition algorithm called 'oblique-extrema empirical mode decomposition' to settle these problems. With this algorithm, any signal can be decomposed into a finite number of 'oblique-extrema intrinsic mode functions' which may possess better-behaved Hilbert transforms and produce more accurate instantaneous frequencies. It can suppress the effect of hidden scales and gets one step further in extracting finer scales. Experimental results demonstrate good performances of this new method.