Fast ensemble empirical mode decomposition for speech-like signal analysis using shaped noise addition

Empirical mode decomposition (EMD) is one of the useful approaches for processing nonlinear and non-stationary signals. However, its shortcomings include mode mixing and end effects that usually appear in the decomposed bands. Although a noise-assisted data analysis (NADA) called ensemble empirical mode decomposition (EEMD) has been proposed to circumvent this problem, doing so also results in an inevitably long computation for alleviating the mode mixing. In this paper, we use shaped noise instead of white noise as a disturbance for a fast convergence of EEMD. The signal-spectrum-dependent noise (SSDN) is able to effectively randomize the targeted signal in time domain, and then significantly save the superfluous calculation around the corresponding energy-free frequencies. The experimental results also show that both pink noise and brown noise outperform the white noise in terms of computation for the EEMD of speech-like signal.