Les récentes évolutions des technologies de l'information et de la communication font qu'il est aujourd'hui facile de consulter des catalogues de morceaux de musique conséquents. De nouvelles représentations et de nouveaux algorithmes doivent de ce fait être développés afin de disposer d'une vision représentative de ces catalogues et de naviguer avec agilité dans leurs contenus. Ceci nécessite une caractérisation efficace des morceaux de musique par l'intermédiaire de descriptions macroscopiques pertinentes. Dans cette thèse, nous nous focalisons sur l'estimation de la structure des morceaux de musique : il s'agit de produire pour chaque morceau une description de son organisation par une séquence de quelques dizaines de segments structurels, définis par leurs frontières (un instant de début et un instant de fin) et par une étiquette représentant leur contenu sonore.La notion de structure musicale peut correspondre à de multiples acceptions selon les ...

Dans cette thèse, nous explorons premièrement la notion de directionnalité lors de la conception de repères d'ondelettes du plan. Cette propriété, qui semble essentielle pour la vision biologique, donne lieu à une meilleure représentation des contours d'ob jets dans les décompositions d'images utilisant ces repères. Elle génère en outre une redondance supplémentaire qui, exploitée à bon escient, permet par exemple de réduire les effets d'un bruit additif (gaussien). Nous montrons également comment cette directionnalité, généralement perçue comme un paramètre figé, peut être adaptée localement aux éléments d'une image. Nous définissons ainsi le concept d'analyse d'images multisélective. Dans ce cadre, des règles de récurrence héritées d'une analyse multirésolution circulaire associent des ondelettes d'une certaine sélectivité angulaire pour générer des ondelettes de plus faible directionnalité jusqu'à l'obtention d'une ondelette totalement isotrope. Dans le cas d'un repère d'ondelettes linéaire, ces différents niveaux de ...

L¢objectif de cette these consiste en l¢etude et la construction de nouvelles transformees scalables mises en jeu dans le schema de codage video t+2D, afin d¢ameliorer le gain en compression. L¢utilisation du formalisme lifting lors de la construction de ces transformees spatio-temporelles permet l¢introduction d¢operateurs non-lineaires, particulierement utiles pour representer efficacement les singularites et discontinuites presentes dans une sequence video. Nous nous interessons dans un premier temps a l¢optimisation et a la construction de nouvelles transformees temporelles compensees en mouvement, afin d¢augmenter l¢efficacite de codage objective et subjective. Nous envisageons ensuite l¢elaboration et la mise en place de bancs de filtres M-bandes pour decomposer spatialement les sous-bande temporelles. Nous traitons alors de l¢extension des proprietes de scalabilite du banc de synthese M-bandes a des facteurs rationnels quelconques. Enfin, nous decrivons la construction de decompositions spatiales en ondelettes adaptatives, non-lineaires et ...

In French. See thesis pdf.

Matrices, as natural representation of linear mappings in finite dimension, play a crucial role in signal processing and machine learning. Multiplying a vector by a full rank matrix a priori costs of the order of the number of non-zero entries in the matrix, in terms of arithmetic operations. However, matrices exist that can be applied much faster, this property being crucial to the success of certain linear transformations, such as the Fourier transform or the wavelet transform. What is the property that allows these matrices to be applied rapidly ? Is it easy to verify ? Can weapproximate matrices with ones having this property ? Can we estimate matrices having this property ? This thesis investigates these questions, exploring applications such as learning dictionaries with efficient implementations, accelerating the resolution of inverse problems or Fast Fourier Transform on graphs.

An intelligent antenna is actually composed of a series of elementary antennas (linear, circular, etc.) who’s received signals are balanced and combined by using a technique of adaptation in order to control and improve the reception or the transmission. The objective of our study is to look further into the knowledge of the methods of formation of the beams and to elaborate a digital technique of synthesis for the formation of ways and the cancellation of interfering which answers the specifications imposed by the system adaptive by using the networks of neurons. The work of thesis consisted of a contribution to the optimization of the lobe of radiation for an intelligent antenna. In a first stage, two complementary approaches were developed to implement the technique of forming of the lobe, one based on an algorithm of optimization which calculates the ...

This work deals with the use of an antenna array at the base station of a mobile communication system for transmission. In reception, solutions that exploit the antenna array are now well established. In transmission, however, the problem remains open. Two approaches are possible : exploit the array by using beamforming techniques or by using diversity techniques. These two approaches are based on opposite assumptions about the channels correlation, which implies a greater or smaller distance between antennas, depending on the environment. In practice, these assumptions are not verified. Here, we aim to deal with the problem as a whole for better exploiting the antenna array. This work treats the mono-user case, as well as the multi-user scenario. In the mono-user case, we propose a transmission scheme composed of a classical transmit diversity technique applied to virtual antennas, which are ...

The goal of this work is to propose a general approach for surveillance video indexing and retrieval. Based on the hypothesis that videos are preprocessed by an external video analysis module, this approach is composed of two phases : indexing phase and retrieval phase. In order to profit from the output of various video analysis modules, a general data model consisting of two main concepts, objects and events, is proposed. The indexing phase that aims at preparing data defined in the data model performs three tasks. Firstly, two new key blob detection methods in the object representation task choose for each detected object a set of key blobs associated with a weight. Secondly, the feature extraction task analyzes a number of visual and temporal features on detected objects. Finally, the indexing task computes attributes of the two concepts and stores ...

The next generation of wireless communication networks will facilitate the connection of a large number of devices and a broad range of services. Serving such a large amount of user equipment (UEs) can be of very high cost if using active antenna solutions such as increasing the number of base stations (BSs), using massive multiple-input multiple-output (MIMO) antennas, and deploying relays between the BSs and the UEs. In this context, a passive antenna solution, such as reconfigurable intelligent surfaces (RISs), would be more beneficial and attractive. RIS has become an emerging technology with diverse applications in future wireless networks, owing to its ability to dynamically control and optimize the propagation environment. The rapid evolution driven by escalating performance demands of coverage in blocked line-of-sight (LOS) scenarios has prompted the exploration of RIS. Motivated by the potential benefits of RIS in ...

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This thesis interests in different methods of image compression combining both Bayesian aspects and ``sparse decomposition'' aspects. Two compression methods are in particular investigated. Transform coding, first, is addressed from a transform optimization point of view. The optimization is considered at two levels: in the spatial domain by adapting the support of the transform, and in the transform domain by selecting local bases among finite sets. The study of bases learned with an algorithm from the literature constitutes an introduction to a novel learning algorithm, which encourages the sparsity of the decompositions. Predictive coding is then addressed. Motivated by recent contributions based on sparse decompositions, we propose a novel Bayesian prediction algorithm based on mixtures of sparse decompositions. Finally, these works allowed to underline the interest of structuring the sparsity of the decompositions. For example, a weighting of the decomposition ...

The amount of digital music available both on the Internet and by each listener has considerably raised for about ten years. The organization and the accessibillity of this amount of data demand that additional informations are available, such as artist, album and song names, musical genre, tempo, mood or other symbolic or semantic attributes. Automatic music indexing has thus become a challenging research area. If some tasks are now correctly handled for certain types of music, such as automatic genre classification for stereotypical music, music instrument recoginition on solo performance and tempo extraction, others are more difficult to perform. For example, automatic transcription of polyphonic signals and instrument ensemble recognition are still limited to some particular cases. The goal of our study is not to obain a perfect transcription of the signals and an exact classification of all the instruments ...

Resistivity distribution estimation, widely known as Electrical Impedance Tomography (EIT), is a non linear ill-posed inverse problem. However, the partial derivative equation ruling this experiment yields no analytical solution for arbitrary conductivity distribution. Thus, solving the forward problem requires an approximation. The Finite Element Method (FEM) provides us with a computationally cheap forward model which preserves the non linear image-data relation and also reveals sufficiently accurate for the inversion. Within the Bayesian approach, Markovian priors on the log-conductivity distribution are introduced for regularization. The neighborhood system is directly derived from the FEM triangular mesh structure. We first propose a maximum a posteriori (MAP) estimation with a Huber-Markov prior which favours smooth distributions while preserving locally discontinuous features. The resulting criterion is minimized with the pseudo-conjugate gradient method. Simulation results reveal significant improvements in terms of robustness to noise, computation rapidity ...

Nowadays, energy efficiency (EE) for wireless communications is becoming a main economical and societal challenge. This thesis studies resource allocation (RA) in EE optimized wireless communication systems for various system models. The main body of this thesis consists of three parts. In the first part, EE optimization is studied in multiple input multiple output (MIMO)-orthogonal frequency-division multiplexing (OFDM) systems. Two special scenarios are considered respectively in two chapters: rate-dependent circuit power and nonlinear distortion at the high power amplifier (HPA). For the first scenario, the condition of the rate-dependent circuit power such that the global optimal EE is guaranteed to be found is given. The impact of system parameters on EE are analyzed. For the second scenario, the condition of parameters of HPA such that the rate function is concave is given. In the second part, EE maximized subcarrier allocation ...

Retrieval of images that lack a (suitable) annotations cannot be achieved through (traditional) Information Retrieval (IR) techniques. Access through such collections can be achieved through the application of computer vision techniques on the IR problem, which is baptized Content-Based Image Retrieval (CBIR). In contrast with most purely technological approaches, the thesis Human-Centered Content-Based Image Retrieval approaches the problem from a human/user centered perspective. Psychophysical experiments were conducted in which people were asked to categorize colors. The data gathered from these experiments was fed to a Fast Exact Euclidean Distance (FEED) transform (Schouten & Van den Broek, 2004), which enabled the segmentation of color space based on human perception (Van den Broek et al., 2008). This unique color space segementation was exploited for texture analysis and image segmentation, and subsequently for full-featured CBIR. In addition, a unique CBIR-benchmark was developed (Van ...