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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...
Empirical mode decomposition (EMD) allows decomposing an observed multicomponent signal into a set of monocomponent signals called Intrinsic Mode Functions (IMFs). EMD provides a large number of IMFs and it is important to select the fundamental IMFs and eliminate the redundant ones. This paper proposes a new criterion, based simultaneously on the Minkowski distance and the Jensen Rényi divergence...
This paper focuses on a pitch estimation method of noisy speech signal using the combination of empirical mode decomposition (EMD) and discrete Fourier transform (DFT). The noisy speech signal is filtered within the range of fundamental frequency. Normalized autocorrelation function (NACF) is computed from the pre-filtered noisy speech signal. The NACF is decomposed by EMD to generate a finite number...
Semi-parametric regression model prediction method based on empirical mode decomposition was studied in this paper. Firstly, basic idea of the empirical mode decomposition was introduced, and the improved algorithm was proposed to mitigate the end effect in the iterative shift process. Secondly, least squares method was employed to estimate the parameter β based on the trend component of empirical...
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