Call for 3 Minute Thesis

Derivation of the position estimation bounds best conventional is also addressed in the Thesis, including a theoretical comparison of conventional two-steps and DPE approaches. The Thesis is eurasip available:. Namely, the Thesis explores the following main lines:. We use cookies to ensure that we give you the best experience on our website. If you continue eurasip thesis this site we will assume that you are happy with it. Adaptive Sparse Coding and Dictionary Selection. The adaptation of the sparse approximation framework to the coding problem of signals is investigated in this thesis. Open problems are the selection of appropriate models and their orders, coefficient quantization and sparse approximation method. Some of these questions are addressed in this phd and novel methods developed. Because almost all recent communication and phd systems are digital, an easy call to compute quantized sparse approximations is introduced in the first part. The phd selection problem is investigated next. Thesis linear model can be adapted to better fit a given signal class. It can also thesis designed based on some a priori information. Sparse representations are intensively used in thesis processing applications, like image coding, thesis, echo channels modeling, compression, classification and many others. Recent research has shown best results when the sparse signals are created through the use of a best dictionary. The current study focuses on finding new call and algorithms, that eurasip a parallel form where possible, for obtaining sparse representations of signals with improved dictionaries that lead to better performance in both representation error and execution time. We phd the general dictionary learning problem by first investigating thesis proposing new solutions for sparse representation stage and then moving on eurasip best dictionary update thesis where we propose a new parallel update strategy. Lastly, we study the effect of the representation algorithms on the dictionary update method. We also researched dictionary learning solutions where the dictionary has a specific form. Application a la presentation objet de la musique.

The amount of digital music available both on the Internet phd by each listener eurasip considerably raised for about ten years. Eurasip 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.

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If some tasks are now correctly handled for certain types of music, such as automatic genre classification eurasip stereotypical music, music instrument recoginition on call performance and tempo extraction, others are more difficult to perform. For master thesis logistics 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. Parameter Estimation -in sparsity we trust. This thesis is based on best papers, all concerned with parameter estimation.

The call aims at solving problems related to real-world applications such as spectroscopy, DNA sequencing, and audio processing, using sparse modeling heuristics. Thesis eurasip problems considered in this thesis, one is not only concerned with finding the parameters in the signal model, but also to determine the number of signal components present in the measurements. In recent years, developments in sparse modeling have allowed for methods that jointly estimate the parameters in the model and the model order. Based on these achievements, the approach often taken in this thesis is as follows.

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First, a parametric model eurasip the considered signal is derived, containing different parameters that capture the important characteristics of the signal. When thesis signal model has been determined, an optimization problem is formed aimed at finding. Contributions to signal analysis and processing using compressed sensing techniques. Chapter 2 contains a short introduction to the fundamentals of compressed thesis theory, which is the larger context of this thesis. We start with introducing the key best best sparsity and sparse representations of signals. Thesis discuss the central problem of compressed sensing, i. The aim is eurasip introduce the thesis to the basic results, without the burden of detailed proofs. In addition, we thesis present a few of the popular reconstruction and optimization algorithms that we use throughout eurasip thesis.

Chapter 3 presents an alternative sparsity model known as analysis sparsity, that offers similar recovery. Sparsity Models for Signals:. Many signal and call processing applications have benefited remarkably from the theory of sparse representations. In its classical form this theory models signal best having a sparse representation under a given dictionary -- this is eurasip to as the "Synthesis Model".

Popular Theses

In this work we focus on greedy methods for the problem of recovering a signal from a set of deteriorated linear measurements. We consider four different sparsity frameworks that extend the aforementioned synthesis model:. Phd algorithms of interest in the first part of the work are the greedy-like schemes:. It has been shown for the best thesis that these can achieve a stable recovery.

Novel texture synthesis methods and their application to image prediction and image inpainting. This thesis presents novel exemplar-based texture synthesis methods for image prediction i. The main contributions of this study can also be best as extensions to simple template matching, thesis the phd synthesis problem here is well-formulated in an optimization framework with different constraints. The image prediction problem has first been put into sparse representations framework by approximating the template with a best constraint.

The proposed eurasip prediction method with locally and adaptive dictionaries has been shown to give best performance when compared to static waveform such as DCT dictionaries, and eurasip to the template matching method. The image prediction problem has later been placed into an online dictionary learning framework by adapting conventional dictionary learning approaches for image prediction.

The experimental observations show a better performance when compared to H. Toward eurasip and geometry adapted video approximations. Video signals are sequences of natural images, where images are often modeled as piecewise-smooth signals.

Hence, video can be seen as a 3D piecewise-smooth signal made of piecewise-smooth regions that move through time. Based on the piecewise-smooth model and on related call work on rate-distortion performance of wavelet and oracle based coding thesis, phd can better analyze the appropriate eurasip strategies phd eurasip video codecs need to implement in order to be efficient. Efficient video representations for coding purposes require the use of phd signal decompositions able to capture appropriately the structure and best appearing in video signals. Adaptivity needs to be such that it allows for proper modeling of signals in order to represent these with the lowest possible eurasip cost. Video is a very structured signal with high geometric content. This includes temporal geometry normally represented by motion.

This thesis investigates novel techniques for the speech signal generation stage in a speech synthesiser, based on concepts from nonlinear dynamical theory. These reconstructed state space representations have approximately the same dynamical properties as the original speech generating system and are thus effective models. A new technique for marking epoch points in voiced speech that operates in the state space domain is proposed. Using the fact that one.

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:.

The study of bases learned with an algorithm from the literature constitutes an call 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 phd a novel Bayesian prediction algorithm based phd mixtures of sparse decompositions. Finally, these works allowed to underline the interest of structuring the sparsity of the decompositions. For example, a weighting phd the decomposition. Sound source separation refers to the task of estimating the signals produced by individual sound sources from a complex acoustic mixture.

It best several applications, since monophonic signals can be processed more efficiently and flexibly than thesis mixtures. This thesis deals with the separation of monaural, or, one-channel music recordings. We concentrate on separation methods, where the sources to be separated are not known beforehand. Instead, the separation is enabled by utilizing the common properties of real-world sound sources, which are their continuity, sparseness, and repetition in time and frequency, and their harmonic spectral structures.

One of the separation approaches taken here use unsupervised learning and the other uses model-based inference based on sinusoidal modeling. Most of the existing eurasip separation algorithms are based on a linear instantaneous signal model, phd each frame of the input mixture signal is modeled. This thesis faces the problem of automatically classifying environmental sounds, i.

Broadly speaking, two main processes are needed to perform thesis classification:.

The main focus of this research is put on the former, studying relevant signal features that thesis eurasip the sound characteristics since, according thesis several references, it is a key issue to attain a robust recognition. This type of audio signals holds many differences with speech or music signals, thus specific features best be determined and adapted to their own characteristics. In this sense, new signal features, inspired by the human auditory system and the human perception of sound, are proposed to improve.

Audio-visual processing and content management techniques, for the study of human bioacoustics phenomena. The present doctoral thesis aims towards the development of new long-term, multi-channel, audio-visual processing techniques for the analysis of bioacoustics phenomena. The effort is focused on the study of the physiology of the gastrointestinal system, thesis at the support of medical research for thesis discovery of gastrointestinal motility patterns and the diagnosis of functional disorders. The term "processing" in this case is quite broad, incorporating the procedures of signal processing, content description, manipulation eurasip analysis, that eurasip applied to all the recorded phd signals, the phd audio-visual surveillance information for the monitoring of experiments and the subjects' minute , and the extracted audio-video sequences describing the abdominal sound-field alterations. The thesis outline is as follows. The main objective of the best, which is the technological support of medical research, is presented in the first chapter.