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Soutenance de thèse d’Imad RIDA le 03/02/17 à 14h à l’INSA, amphi Marie Curie

02/03/2017 @ 14:00 - 17:30

Titre : Temporal Signals Classification

Les rapporteurs sont :

M. David Brie (Université Lorraine)
M. Mounim El Yacoubi (Télécom SudParis)

Les examinateurs sont :

M. Salah Bourennane (École Centrale Marseille)
Mme. Su Ruan (Université Rouen Normandie)
Mme. Marie Szafranski (ENSIIE)

L’ encadrant est :

M. Romain Hérault (LITIS – INSA Rouen Normandie)

et le directeur de thèse est :

M. Gilles Gasso (LITIS – INSA Rouen Normandie)

 

Nowadays, there are a lot of applications related to machine vision and hearing which tried to reproduce human capabilities on machines. These problems are mainly amenable to a temporal signals classification problem, due our interest to this subject. In fact, we were interested to two distinct problems, human gait recognition and audio signal recognition including both environmental and music ones. In the former, we have proposed a novel method to automatically learn and select the dynamic human body-parts to tackle the problem intra-class variations contrary to state-of-art methods which relied on predefined knowledge. To achieve it, a group fused lasso algorithm is applied to segment the human body into parts with coherent motion value across the subjects. In the latter, while no conventional pharmacie-ed.net/Viagra.html feature representation showed its ability to tackle both environmental and music problems, we propose to model audio classification as a supervised dictionary learning problem. This is done by learning a dictionary per class and encouraging the dissimilarity between the dictionaries by penalizing their pairwise similarities. In addition the coefficients of a signal representation over these dictionaries is sought as sparse as possible. The experimental evaluations provide performing and encouraging results.

 

Détails

Date :
02/03/2017
Heure :
14:00 - 17:30

Lieu

INSA Rouen Normandie
Publié dans
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