Variational Mode Decomposition for Python
This is python realization for Variatioanl Mode Decomposition
Authors: Konstantin Dragomiretskiy and Dominique Zosso
signal - the time domain signal (1D) 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
u - the collection of decomposed modes
u_hat - spectra of the modes
omega - estimated mode center-frequencies
K. Dragomiretskiy, D. Zosso, Variational Mode Decomposition, IEEE Trans. on Signal Processing (in press) please check here for update reference: http://dx.doi.org/10.1109/TSP.2013.2288675