Spectrum-based decomposition of a 1D input signal into k band-separated modes. Here, we propose an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently. The model looks for an ensemble of modes and their respective center frequencies, such that the modes collectively reproduce the (1D) input signal, while each being smooth after demodulation into baseband. The variational model is efficiently optimized using an alternating direction method of multipliers approach.

Applications: signal decomposition in audio engineering, climate analysis, various flux and neuromuscular signal analysis in medicine and biology, etc.

This is a variational alternative to the empirical mode decomposition (EMD; Huang et al. 1998), or the empirical wavelet transform (EWT; Gilles 2013).

See: K. Dragomiretskiy and D. Zosso, Variational Mode Decomposition, IEEE Trans. Signal Processing (in press). http://dx.doi.org/10.1109/TSP.2013.2288675