Glottal Source Estimation based on Bivariate Empirical Mode Decomposition

The Bivariate Empirical Mode Decomposition (BEMD) is an extension of Empirical Mode Decomposition (EMD) algorithm. In its classical formulation, the EMD can only be applied to real-valued time series. In this paper, the BEMD algorithm is proposed as an alternative to estimate the glottal source from the speech signal. The bivariate empirical mode decomposition decomposes the complex log spectrum into its real part which represents the magnitude and its imaginary part which represents the phase and guarantees an equal number of real and imaginary parts of oscillatory modes named intrinsic mode functions. An adaptive procedure, based on IMFs variances, is then used to estimate the magnitude of the glottal source by selecting the appropriate intrinsic mode functions that constitute the magnitude of the glottal source in the log spectral domain. The proposed method is tested on synthetic speech signals and compared to the true model of the glottal source for different lengths of the weighting window.