https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Sun, 12 Apr 2026 19:55:55 GMT 2026-04-12T19:55:55Z http://hdl.handle.net/10985/9051 Débruitage fréquentiel de signaux par EMD KOMATY, Ali; DARE-EMZIVAT, Delphine; BOUDRAA, Abdel-Ouahab Dans cet article, nous proposons un nouveau schéma de débruitage des signaux basé sur la décomposition modale empirique associée à une analyse fréquentielle. Le principe de l’approche consiste à seuiller les modes extraits du signal bruité dans le domaine fréquentiel et non dans le domaine temporel comme c’est le cas du débruitage classique. Chaque mode est décomposé en blocs de même taille et le contenu fréquentiel de chacun d’eux est analysé. Le critère de sélection d’un bloc dit "pertinent" repose sur deux seuils, l’un énergétique, le second fréquentiel, seuils obtenus à l’issue d’une phase d’apprentissage. Le signal est alors reconstruit en utilisant tous les modes seuillés. Les performances du nouveau débruitage sont illustrés sur des signaux synthétiques et réels et les résultats comparés à ceux de la littérature. Tue, 01 Jan 2013 00:00:00 GMT http://hdl.handle.net/10985/9051 2013-01-01T00:00:00Z KOMATY, Ali DARE-EMZIVAT, Delphine BOUDRAA, Abdel-Ouahab Dans cet article, nous proposons un nouveau schéma de débruitage des signaux basé sur la décomposition modale empirique associée à une analyse fréquentielle. Le principe de l’approche consiste à seuiller les modes extraits du signal bruité dans le domaine fréquentiel et non dans le domaine temporel comme c’est le cas du débruitage classique. Chaque mode est décomposé en blocs de même taille et le contenu fréquentiel de chacun d’eux est analysé. Le critère de sélection d’un bloc dit "pertinent" repose sur deux seuils, l’un énergétique, le second fréquentiel, seuils obtenus à l’issue d’une phase d’apprentissage. Le signal est alors reconstruit en utilisant tous les modes seuillés. Les performances du nouveau débruitage sont illustrés sur des signaux synthétiques et réels et les résultats comparés à ceux de la littérature. http://hdl.handle.net/10985/10293 On signal denoising by EMD in the frequency domain BAY-AHMED, Hadj-Ahmed; KOMATY, Ali; DARE-EMZIVAT, Delphine; BOUDRAA, Abdel-Ouahab In this work a new denoising scheme based on the empirical mode decomposition associated with a frequency analysis is introduced. Compared to classical approaches where the extracted modes are thresholded in time domain, in the proposed strategy the thresholding is done in the frequency domain. Each mode is divided into blocks of equal length where the frequency content of each one is analyzed. Relevant modes are identified using an energy and a frequency thresholds obtained by training. The denoised signal is obtained by the superposition of the thresholded modes. The effectiveness of the proposed scheme is illustrated on synthetic and real signals and the results compared to those of methods reported recently. Thu, 01 Jan 2015 00:00:00 GMT http://hdl.handle.net/10985/10293 2015-01-01T00:00:00Z BAY-AHMED, Hadj-Ahmed KOMATY, Ali DARE-EMZIVAT, Delphine BOUDRAA, Abdel-Ouahab In this work a new denoising scheme based on the empirical mode decomposition associated with a frequency analysis is introduced. Compared to classical approaches where the extracted modes are thresholded in time domain, in the proposed strategy the thresholding is done in the frequency domain. Each mode is divided into blocks of equal length where the frequency content of each one is analyzed. Relevant modes are identified using an energy and a frequency thresholds obtained by training. The denoised signal is obtained by the superposition of the thresholded modes. The effectiveness of the proposed scheme is illustrated on synthetic and real signals and the results compared to those of methods reported recently. http://hdl.handle.net/10985/10092 Estimation des paramètres d'un processus Symétrique Alpha Stable (S-alpha-Stable) à partir de ses modes empiriques KOMATY, Ali; BOUDRAA, Abdel-Ouahab; DARE-EMZIVAT, Delphine Dans ce travail nous nous intéressons au problème d’estimation des paramètres d’un processus alpha stable symétrique à partir de ses modes empiriques extraits par la décomposition modale empirique multivariée (MEMD). Nous exploitons le fait que le caractère impulsif du bruit est mieux préservé par ses premiers modes empiriques pour estimer son exposant caractéristique ainsi que son facteur d’échelle. Nous montrons que les paramètres du processus sont mieux estimés à partir de ses modes empiriques que du processus lui-même. Des résultats d’estimation des paramères utilisant le MEMD sont présentés et comparés à ceux des estimateurs basés sur les quantiles et la fonction caractéristique empirique. Thu, 01 Jan 2015 00:00:00 GMT http://hdl.handle.net/10985/10092 2015-01-01T00:00:00Z KOMATY, Ali BOUDRAA, Abdel-Ouahab DARE-EMZIVAT, Delphine Dans ce travail nous nous intéressons au problème d’estimation des paramètres d’un processus alpha stable symétrique à partir de ses modes empiriques extraits par la décomposition modale empirique multivariée (MEMD). Nous exploitons le fait que le caractère impulsif du bruit est mieux préservé par ses premiers modes empiriques pour estimer son exposant caractéristique ainsi que son facteur d’échelle. Nous montrons que les paramètres du processus sont mieux estimés à partir de ses modes empiriques que du processus lui-même. Des résultats d’estimation des paramères utilisant le MEMD sont présentés et comparés à ceux des estimateurs basés sur les quantiles et la fonction caractéristique empirique. http://hdl.handle.net/10985/8922 EMD-Based Filtering Using Similarity Measure Between Probability Density Functions of IMFs KOMATY, Ali; BOUDRAA, Abdel-Ouahab; AUGIER, Benoit; DARE-EMZIVAT, Delphine This paper introduces a new signal-filtering which combines the empirical mode decomposition (EMD) and a similarity measure. A noisy signal is adaptively broken down into oscillatory components called intrinsic mode functions (IMFs) by EMD followed by an estimation of the probability density function (pdf) of each extracted mode. The key idea of this paper is to make use of partial reconstruction, the relevant modes being selected on the basis of a striking similarity between the pdf of the input signal and that of each mode. Different similarity measures are investigated and compared. The obtained results, on simulated and real signals, show the effectiveness of the pdf-based filtering strategy for removing both white Gaussian and colored noises and demonstrate its superior performance over partial reconstruction approaches reported in the literature. Wed, 01 Jan 2014 00:00:00 GMT http://hdl.handle.net/10985/8922 2014-01-01T00:00:00Z KOMATY, Ali BOUDRAA, Abdel-Ouahab AUGIER, Benoit DARE-EMZIVAT, Delphine This paper introduces a new signal-filtering which combines the empirical mode decomposition (EMD) and a similarity measure. A noisy signal is adaptively broken down into oscillatory components called intrinsic mode functions (IMFs) by EMD followed by an estimation of the probability density function (pdf) of each extracted mode. The key idea of this paper is to make use of partial reconstruction, the relevant modes being selected on the basis of a striking similarity between the pdf of the input signal and that of each mode. Different similarity measures are investigated and compared. The obtained results, on simulated and real signals, show the effectiveness of the pdf-based filtering strategy for removing both white Gaussian and colored noises and demonstrate its superior performance over partial reconstruction approaches reported in the literature. http://hdl.handle.net/10985/8939 Speech enhancement using empirical mode decomposition and the Teager–Kaiser energy operator; Rehaussement du signal de parole par EMD et opérateur de Teager-Kaiser KHALDI, Kais; BOUDRAA, Abdel-Ouahab; KOMATY, Ali In this paper a speech denoising strategy based on time adaptive thresholding of intrinsic modes functions (IMFs) of the signal, extracted by empirical mode decomposition (EMD), is introduced. The denoised signal is reconstructed by the superposition of its adaptive thresholded IMFs. Adaptive thresholds are estimated using the Teager–Kaiser energy operator (TKEO) of signal IMFs. More precisely, TKEO identifies the type of frame by expanding differences between speech and non-speech frames in each IMF. Based on the EMD, the proposed speech denoising scheme is a fully data-driven approach. The method is tested on speech signals with different noise levels and the results are compared to EMD-shrinkage and wavelet transform (WT) coupled with TKEO. Speech enhancement performance is evaluated using output signal to noise ratio (SNR) and perceptual evaluation of speech quality (PESQ) measure. Based on the analyzed speech signals, the proposed enhancement scheme performs better than WT-TKEO and EMD-shrinkage approaches in terms of output SNR and PESQ. The noise is greatly reduced using time-adaptive thresholding than universal thresholding. The study is limited to signals corrupted by additive white Gaussian noise. The authors would like to thank Professor Mohamed Bahoura from Universite de Quebec a Rimouski for fruitful discussions on time adaptive thresholding Wed, 01 Jan 2014 00:00:00 GMT http://hdl.handle.net/10985/8939 2014-01-01T00:00:00Z KHALDI, Kais BOUDRAA, Abdel-Ouahab KOMATY, Ali In this paper a speech denoising strategy based on time adaptive thresholding of intrinsic modes functions (IMFs) of the signal, extracted by empirical mode decomposition (EMD), is introduced. The denoised signal is reconstructed by the superposition of its adaptive thresholded IMFs. Adaptive thresholds are estimated using the Teager–Kaiser energy operator (TKEO) of signal IMFs. More precisely, TKEO identifies the type of frame by expanding differences between speech and non-speech frames in each IMF. Based on the EMD, the proposed speech denoising scheme is a fully data-driven approach. The method is tested on speech signals with different noise levels and the results are compared to EMD-shrinkage and wavelet transform (WT) coupled with TKEO. Speech enhancement performance is evaluated using output signal to noise ratio (SNR) and perceptual evaluation of speech quality (PESQ) measure. Based on the analyzed speech signals, the proposed enhancement scheme performs better than WT-TKEO and EMD-shrinkage approaches in terms of output SNR and PESQ. The noise is greatly reduced using time-adaptive thresholding than universal thresholding. The study is limited to signals corrupted by additive white Gaussian noise. http://hdl.handle.net/10985/9046 On the behavior of EMD and MEMD in presence of symmetric alpha-stable noise KOMATY, Ali; BOUDRAA, Abdel-Ouahab; NOLAN, John; DARE-EMZIVAT, Delphine EmpiricalMode Decomposition (EMD) and its extended versions such as Multivariate EMD (MEMD) are data-driven techniques that represent nonlinear and non-stationary data as a sum of a finite zero-mean AM-FM components referred to as Intrinsic Mode Functions (IMFs). The aim of this work is to analyze the behavior of EMD and MEMD in stochastic situations involving non-Gaussian noise, more precisely, we examine the case of Symmetric Alpha-Stable noise. We report numerical experiments supporting the claim that both EMD and MEMD act, essentially, as filter banks on each channel of the input signal in the case of Symmetric Alpha Stable noise. Reported results show that, unlike EMD, MEMD has the ability to align common frequency modes across multiple channels in same index IMFs. Further, simulations show that, contrary to EMD, for MEMD the stability property is well satisfied for the modes of lower indices and this result is exploited for the estimation of the stability index of the Symmetric Alpha Stable input signal. Thu, 01 Jan 2015 00:00:00 GMT http://hdl.handle.net/10985/9046 2015-01-01T00:00:00Z KOMATY, Ali BOUDRAA, Abdel-Ouahab NOLAN, John DARE-EMZIVAT, Delphine EmpiricalMode Decomposition (EMD) and its extended versions such as Multivariate EMD (MEMD) are data-driven techniques that represent nonlinear and non-stationary data as a sum of a finite zero-mean AM-FM components referred to as Intrinsic Mode Functions (IMFs). The aim of this work is to analyze the behavior of EMD and MEMD in stochastic situations involving non-Gaussian noise, more precisely, we examine the case of Symmetric Alpha-Stable noise. We report numerical experiments supporting the claim that both EMD and MEMD act, essentially, as filter banks on each channel of the input signal in the case of Symmetric Alpha Stable noise. Reported results show that, unlike EMD, MEMD has the ability to align common frequency modes across multiple channels in same index IMFs. Further, simulations show that, contrary to EMD, for MEMD the stability property is well satisfied for the modes of lower indices and this result is exploited for the estimation of the stability index of the Symmetric Alpha Stable input signal.