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In this paper an algorithm based on the ensemble empirical mode decomposition (EEMD) is presented. The key idea on the EEMD relies on averaging the modes obtained by EMD applied to several realizations of Gaussian white noise added to the original signal. The resulting decomposition solves the EMD mode mixing problem, however it introduces new ones. In the(More)
Empirical mode decomposition (EMD) is an algorithm for signal analysis recently introduced by Huang. It is a completely data-driven non-linear method for the decomposition of a signal into AM-FM components. In this paper two new EMD-based methods for the analysis and classification of pathological voices are presented. They are applied to speech signals(More)
Received 25 Revised Accepted 27 This work presents a discussion on the probability density function of Intrinsic Mode Functions (IMFs) provided by the Empirical Mode Decomposition of Gaussian white 29 noise, based on experimental simulations. The influence on the probability density functions of the data length and of the maximum allowed number of(More)
Several feature extraction techniques have been proposed for pathological voice analysis and classification [1–5]. Most of them use measures that characterize different aspects of the voice signal, such as frequency perturbations and noise content. In these cases, a vector representation of the data is often chosen whose size impedes the data's(More)
— A new algorithm for pitch extraction based on the Ensemble Empirical Mode Decomposition (EEMD) is presented. Applications to normal and pathological voices are considered. EEMD is a completely data-driven method for signal decomposition into a sum of AM-FM components, called Intrinsic Mode Functions (IMFs) or modes, which can be written as ()cos(()) A t t(More)
In this work, a new instantaneous fundamental frequency extraction method is presented, with the attention especially focused on its robustness for pathological voices processing. It is based on the Ensemble Empirical Mode Decomposition (EEMD) algorithm, which is a completely data-driven method for signal decomposition into a sum of AM - FM components,(More)
In this work we explore the capabilities of two noise-assisted EMD methods: Ensemble EMD (EEMD) and the recently proposed Complete Ensemble EMD with Adaptive Noise (CEEMDAN), to recover a pure tone embedded in different kinds of noise, both stationary and nonstationary. Experiments are carried out for assessing their performances with respect to the level(More)
Techniques for the visualization of high-dimensional data are common in exploratory data analysis and can be very useful for gaining an intuition into the structure of a data set. The classical method of principal component analysis is the one most often employed, however in recent years a number of other nonlinear techniques have been introduced. In the(More)