Variational Mode Decomposition (VMD) and Its Variants

The orginal VMD code: VMD.m

K. Dragomiretskiy, D. Zosso, Variational Mode Decomposition, IEEE Trans. on Signal Processing

The Multivariate Variational Mode Decomposition code: MVVMD.m

N. Rehman, H. Aftab, Multivariate Variational Mode Decomposition, arXiv:1907.04509, 2019.

Our works: MVMD.p, STMVMD.p, MAC.p, MVMD.pyd, STMVMD.pyd. Only pcodes for Matlab R2016a and pydcodes for Python 3.6.5 are available now. Please note: we only permit to use these programs to verify our paper, "Multi-dimensional Variational Mode Decomposition and Its Short-time Counterpart". Other purposes are not permitted until further notice. If you have any questions regarding the above codes, please contact me at liushuaishuai_hit@163.com.

Input and Parameters:

signal - the time domain signal to be decomposed

alpha - the balancing parameter of the data-fidelity constraint

tau - time-step of the dual ascent ( pick 0 for noise-slack )

K - the number of modes to be recovered

DC - true if the first mode is put and kept at DC (0-freq)

init - 0 = all omegas start at 0

- 1 = all omegas start uniformly distributed   

- 2 = all omegas initialized randomly

tol - tolerance of convergence criterion; typically around 1e-6

winLen - the number of analysis points of a sliding window

overlap - the number of overlap points of adjacent windows