The sifting process is completed using a time varying filter technique.The local cut-off frequency is adaptively designed by fully facilitating the instantaneous amplitude and frequency information. Then nonuniform B-spline approximation is adopted as a time varying filter. In order to solve the intermittence problem, a cut-off frequency realignment algorithm is also introduced. Aimed at improving the performance under low sampling rates, a bandwidth criterion for intrinsic mode function (IMF) is proposed. TVF-EMD is fully adaptive and suitable for the analysis of linear and non-stationary signals. Compared with EMD, the proposed method is able to improve the frequency separation performance, as well as the stability under low sampling rates. Besides, the proposed method is robust against noise interference.
TVF-EMD is from http://www.sciencedirect.com/science/article/pii/S0165168417301135
To use this code, please cite our work: Li, Heng, Zhi Li, and Wei Mo. "A time varying filter approach for empirical mode decomposition." Signal Processing 138 (2017): 146-158.
shiyuan li (2021). Time varying filter based empirical mode decomposition(TVF-EMD) (https://www.mathworks.com/matlabcentral/fileexchange/63300-time-varying-filter-based-empirical-mode-decomposition-tvf-emd), MATLAB Central File Exchange. Retrieved .
At first I would like to thank you for sharing toolbox with us.
I have been struggling to use this toolbox for discrete functions.like data that logged by the sensors.
Thank you for your response..
How can we get the amplitude contour using 'spectrogram_emd'? Once it is done how can we extend it to find the Hilbert Marginal Spectrum?
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