Voice Identity Finder Using the Back Propagation Algorithm of an Artificial Neural Network

AbstractVoice recognition systems are used to distinguish different sorts of voices. However, recognizing a voice is not always successful due to the presence of different parameters. Hence, there is a need to create a set of estimation criteria and a learning process using Artificial Neural Network (ANN). The learning process performed using ANN allows the system to mimic how the brain learns to understand and differentiate among voices. The key to undergo this learning is to specify the free parameters that will be adapted through this process of simulation. Accordingly, this system will store the knowledge processed after performing the back propagation learning and will be able to identify the corresponding voices. The proposed learning allows the user to enter a number of different voices to the system through a microphone

Filtrage adaptatif à l'aide de méthodes à noyau (application au contrôle d'un palier magnétique actif)

L estimation fonctionnelle basée sur les espaces de Hilbert à noyau reproduisant demeure un sujet de recherche actif pour l identification des systèmes non linéaires. L'ordre du modèle croit avec le nombre de couples entrée-sortie, ce qui rend cette méthode inadéquate pour une identification en ligne. Le critère de cohérence est une méthode de parcimonie pour contrôler l ordre du modèle. Le modèle est donc défini à partir d'un dictionnaire de faible taille qui est formé par les fonctions noyau les plus pertinentes.Une fonction noyau introduite dans le dictionnaire y demeure même si la non-stationnarité du système rend sa contribution faible dans l'estimation de la sortie courante. Il apparaît alors opportun d'adapter les éléments du dictionnaire pour réduire l'erreur quadratique instantanée et/ou mieux contrôler l'ordre du modèle.La première partie traite le sujet des algorithmes adaptatifs utilisant le critère de cohérence. L'adaptation des éléments du dictionnaire en utilisant une méthode de gradient stochastique est abordée pour deux familles de fonctions noyau. Cette partie a un autre objectif qui est la dérivation des algorithmes adaptatifs utilisant le critère de cohérence pour identifier des modèles à sorties multiples.La deuxième partie introduit d'une manière abrégée le palier magnétique actif (PMA). La proposition de contrôler un PMA par un algorithme adaptatif à noyau est présentée pour remplacer une méthode utilisant les réseaux de neurones à couches multiplesFunction approximation methods based on reproducing kernel Hilbert spaces are of great importance in kernel-based regression. However, the order of the model is equal to the number of observations, which makes this method inappropriate for online identification. To overcome this drawback, many sparsification methods have been proposed to control the order of the model. The coherence criterion is one of these sparsification methods. It has been shown possible to select a subset of the most relevant passed input vectors to form a dictionary to identify the model.A kernel function, once introduced into the dictionary, remains unchanged even if the non-stationarity of the system makes it less influent in estimating the output of the model. This observation leads to the idea of adapting the elements of the dictionary to obtain an improved one with an objective to minimize the resulting instantaneous mean square error and/or to control the order of the model.The first part deals with adaptive algorithms using the coherence criterion. The adaptation of the elements of the dictionary using a stochastic gradient method is presented for two types of kernel functions. Another topic is covered in this part which is the implementation of adaptive algorithms using the coherence criterion to identify Multiple-Outputs models.The second part introduces briefly the active magnetic bearing (AMB). A proposed method to control an AMB by an adaptive algorithm using kernel methods is presented to replace an existing method using neural networksTROYES-SCD-UTT (103872102) / SudocSudocFranceF

Diamagnetism and density-wave order in the pseudogap regime of YBa 2 Cu 3 O 6 + x

Clear experimental evidence of charge density-wave correlations competing with superconducting order in YBCO have thrust their relationship with the pseudogap regime into the spotlight. To aid in characterizing the pseudogap regime, we propose a dimensionless ratio of the diamagnetic susceptibility to the correlation length of the charge density-wave correlations. Using Monte Carlo simulations, we compute this ratio on the classical model of Hayward et al. [Science 343, 1336 (2014)], which describes angular fluctuations of a multicomponent order, capturing both superconducting and density-wave correlations. We compare our results with available data on YBa2Cu3O6+x, and propose experiments to clarify the value of this dimensionless ratio using existing samples and techniques.Physic

The Experience of HR practices and processes in organisations in Lebanon

This study aims to find out how Lebanese culture influences HRM practices within different types of organisations operating in Lebanon. It explores the effects of globalization on the work environment in Lebanon, the distinctive features of the Lebanese HRM practices, and the ways in which best practice may be considered useful in the context of HRM in the country. The research integrates qualitative and quantitative data in a sequential exploratory strategy.This was achieved by gathering quantitative data from an online survey targeting Lebanese HR professionals via social media networks. The survey was followed by semi-structured interviews with professionals, the majority of them responsible for HR in their organisations. The research highlights how HR practice is fundamentally informed by the particular cultural context of Lebanon, and that this has implications on the development of HRM as a profession,which has not yet achieved the status of essential, objective and strategic occupation.The study contributes to the limited knowledge on human resource management practices in Lebanon and the literature around the impact of national culture on the organisation and its HR policies and practices. The thesis also adds to the literature around the segregation in employment on the basis of gender. In particular, the study fills gaps in the literature about the segregation at work based on marital status and age. In addition to the novel technique of using a professional social network such as LinkedIn to collect valuable quantitative data, the results produced by this research can be used by Lebanese HR professionals for the development and improvement of HRM practices at their organisations.</div

Online kernel adaptive algorithms with dictionary adaptation for MIMO models

International audienceNonlinear system identification has always been a challenging problem. The use of kernel methods to solve such problems becomes more prevalent. However, the complexity of these methods increases with time which makes them unsuitable for online identification. This drawback can be solved with the introduction of the coherence criterion. Furthermore, dictionary adaptation using a stochastic gradient method proved its efficiency. Mostly, all approaches are used to identify Single Output models which form a particular case of real problems. In this letter we investigate online kernel adaptive algorithms to identify Multiple Inputs Multiple Outputs model as well as the possibility of dictionary adaptation for such models

DICTIONARY ADAPTATION FOR ONLINE PREDICTION OF TIME SERIES DATA WITH KERNELS

During the last few years, kernel methods have been very useful to solve nonlinear identification problems. The main drawback of these methods resides in the fact that the number of elements of the kernel development, i.e., the size of the dictionary, increases with the number of input data, making the solution not suitable for online problems especially time series applications. Recently, Richard, Bermudez and Honeine investigated a method where the size of the dictionary is controlled by a coherence criterion. In this paper, we extend this method by adjusting the dictionary elements in order to reduce the residual error and/or the average size of the dictionary. The proposed method is implemented for time series prediction using the kernel-based affine projection algorithm. Index Terms — Nonlinear adaptive filters, machine learning, nonlinear systems, kernel methods. 1

Nonlinear Adaptive Filtering using Kernel-based Algorithms with Dictionary Adaptation

International audienceNonlinear adaptive filtering has been extensively studied in the literature, using, for example, Volterra filters or neural networks. Recently, kernel methods have been offering an interesting alternative because they provide a simple extension of linear algorithms to the nonlinear case. The main drawback of online system identification with kernel methods is that the filter complexity increases with time, a limitation resulting from the representer theorem, which states that all past input vectors are required. To overcome this drawback, a particular subset of these input vectors (called dictionary) must be selected to ensure complexity control and good performance. Up to now, all authors considered that, after being introduced into the dictionary, elements stay unchanged even if, because of nonstationarity, they become useless to predict the system output. The objective of this paper is to present an adaptation scheme of dictionary elements, which are considered here as adjustable model parameters, by deriving a gradient-based method under collinearity constraints. The main interest is to ensure a better tracking performance. To evaluate our approach, dictionary adaptation is introduced into three well-known kernel-based adaptive algorithms: kernel recursive least squares, kernel normalized least mean squares, and kernel affine projection. The performance is evaluated on nonlinear adaptive filtering of simulated and real data sets. As confirmed by experiments, our dictionary adaptation scheme allows either complexity reduction or a decrease of the instantaneous quadratic error, or both simultaneously

Adaptation en ligne d'un dictionnaire pour les méthodes à noyau

International audienceCet article traite du problème de l'identification en-ligne des systèmes non linéaires et non stationnaires par les méthodes à noyau. L'ordre des modèles est contrôlé par le critère de cohérence utilisé comme critère de parcimonie, qui mène à sélectionner les fonctions noyau les plus pertinentes au sens de ce critère, formant ainsi un dictionnaire. On exploite l'adaptation du dictionnaire en proposant une méthode de descente de gradient stochastique qui s'applique conjointement à l'estimation en ligne des coefficients du modèle à noyau. un algorithme d'identification en ligne à noyau. Pour ce dernier, sans limitation, il peut s'agir de l'algorithme de moindres carrés récursif à noyau ou de projection affine à noyau. La méthode proposée permet une diminution de l'erreur quadratique instantanée et une réduction de la complexité du modèle