Gastón Schlotthauer

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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 datadriven 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)
GASTÓN SCHLOTTHAUER∗,§, MARÍA EUGENIA TORRES∗,¶, 7 HUGO L. RUFINER†,‖ and PATRICK FLANDRIN‡,// ∗Lab. Signals and Nonlinear Dynamics, Applied Research group 9 on Signal Processing and Pattern Recognition (ARSiPRe) Facultad de Ingenieŕıa, Universidad Nacional de Entre Rı́os 11 Ruta 11 Km 10 Oro Verde, Entre Rı́os 3101, Argentina ∗National Council of(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(More)
For an adequate analysis of pathological speech signals, a sizeable number of parameters is required, such as those related to jitter, shimmer and noise content. Often this kind of high-dimensional signal representation is difficult tounderstand, even for expert voice therapists andphysicians.Data visualization of a high-dimensional dataset can provide a(More)
Heart rate variability is a non invasive and indirect measure of the autonomic control of the heart. Therefore, alterations to this control system caused by myocardial ischaemia are reflected in changes in the complex and irregular fluctuations of this signal. Multifractal analysis is a well suited tool for the analysis of this kind of fluctuations, since(More)
MARCELO A. COLOMINAS∗,‡, GASTÓN SCHLOTTHAUER∗,§, MARÍA E. TORRES∗,¶ and PATRICK FLANDRIN†,‖ ∗CONICET – Laboratorio de Señales y Dinámicas no Lineales Facultad de Ingenieŕıa, Universidad Nacional de Entre Rı́os Ruta 11Km 10, Oro Verde, Entre Rı́os 3100, Argentina †Laboratoire de Physique (UMR 5672 CNRS ) École Normale Supérieure de Lyon 46 allée d’Italie,(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 datadriven method for signal decomposition into a sum of AM FM components, called(More)
Patients with type I diabetes nearly always need therapy with insulin. The most desirable treatment would be to mimic the operation of a normal pancreas. In this work a patient affected with this pathology is modeled and identified with a neural network, and a control strategy known as Nonlinear Model Predictive Control is evaluated as an approach to(More)