ÁREA FINCA DE TIRMA [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte, 201

Allocation binaire et déconvolution psychoacoustique de complexité réduite dans un codeur audio de haute qualité

Les codeurs de musique actuels atteignent de taux de compression supérieurs à 8 sans perte de qualité subjective en suivant le principe : ne pas coder ce que l'oreille n'entend pas. La mise en forme du bruit de codage se fait en deux étapes distinctes : le calcul d'un seuil de masquage à partir de la théorie psychoacoustique, puis l'allocation des ressources binaires en fonction du seuil de masquage. Le calcul du seuil de masquage est un problème difficile qui n'est qu'approché dans les codeurs actuels. Nous montrons que le calcul explicite du seuil de masquage n'est pas nécessaire et nous proposons un algorithme direct à faible complexité réalisant une meilleure approximation de la théorie psychoacoustique

Étude comparative de filtres perceptuels adaptés à des codeurs audio

Les codeurs audio de haute qualité utilisent souvent un modèle psychoacoustique pour prendre en compte les propriétés de l'oreille. On compare des filtres perceptuels, calculés à partir d'une prédiction linéaire, avec des filtres obtenus avec des seuils de masquage utilisés dans des codeurs de musique. Nous avons remarqué que ces derniers ne donnent pas de meilleurs résultats. Si la démarche la plus naturelle consiste à définir un meilleur modèle psychoacoustique, on propose ici une méthode intermédiaire consistant à donner plus de degrés de liberté à une méthode de type standard, en traitant individuellement les zéros du filtre blanchissant

Structuration d’un cours en ligne : l’exemple de SMEL (Statistique Médicale En Ligne)

The tree structure of a traditional course can be completed for an online course by a lexical graph structure. An index layer appears for the user as a turntable that logically connects the main notions of the course. It gives access not only to the set of lecture notes, but also to the other constituents of the course (articles, experimental activities...). For implementation purposes, the index is encoded as a data basis. The entries are the nodes of the graph, and the fields encode the hypertext links. This structure has been implemented for SMEL, an online course of medical statistics.La structure arborescente d’un cours traditionnel peut être complétée pour un cours en ligne par une structure de graphe lexical. Pour l’utilisateur, une couche lexique apparaît comme une plaque tournante qui connecte logiquement les principales notions du cours. Elle permet d’accéder non seulement aux notes rédigées, mais aussi à l’environnement étendu constituant le cours (articles de complément, activités expérimentales...). Pour l’implémentation, le lexique est codé comme une base de données, dont les entrées correspondent aux nœuds du graphe, et dont les différents champs contiennent les informations codant les liens hypertextes. Cette structure a été implémentée pour SMEL, un cours en ligne de statistique médicale.Perreau Guimaraes Marcos, Ycart Bernard. Structuration d’un cours en ligne : l’exemple de SMEL (Statistique Médicale En Ligne). In: Sciences et techniques éducatives, volume 7 n°2, 2000. pp. 413-426

Tikhonov-based Regularization of a Global Optimum Approach of One-layer Neural Networks with Fixed Transfer Function by Convex Optimization

Abstract — Regularization is useful for extending learning models to be effective for classifications. Given the success of regularized-perceptron-based (one-layer neural network) methods, we introduced a similar kind of regularization for two global-optimum approaches recently proposed by Castillo et al, which combined the degree of freedom of using nonlinear transfer functions with the computational efficiency of solving complex problems. We focused on the two approaches that used sigmoid transfer functions. The first linear approach involved solving a set of linear equations, while the second min-max approach was reduced to a linear programming problem. We introduced regularization in such a way that the first linear approach remained linear and had a close form solution, while the second min-max approach was converted from a linear programming into a quadratic programming problem. Electroencephalography recordings were used to show how classifications could be improved. I

Structural Similarities between Brain and Linguistic Data Provide Evidence of Semantic Relations in the Brain

<div><p>This paper presents a new method of analysis by which structural similarities between brain data and linguistic data can be assessed at the semantic level. It shows how to measure the strength of these structural similarities and so determine the relatively better fit of the brain data with one semantic model over another. The first model is derived from WordNet, a lexical database of English compiled by language experts. The second is given by the corpus-based statistical technique of latent semantic analysis (LSA), which detects relations between words that are latent or hidden in text. The brain data are drawn from experiments in which statements about the geography of Europe were presented auditorily to participants who were asked to determine their truth or falsity while electroencephalographic (EEG) recordings were made. The theoretical framework for the analysis of the brain and semantic data derives from axiomatizations of theories such as the theory of differences in utility preference. Using brain-data samples from individual trials time-locked to the presentation of each word, ordinal relations of similarity differences are computed for the brain data and for the linguistic data. In each case those relations that are invariant with respect to the brain and linguistic data, and are correlated with sufficient statistical strength, amount to structural similarities between the brain and linguistic data. Results show that many more statistically significant structural similarities can be found between the brain data and the WordNet-derived data than the LSA-derived data. The work reported here is placed within the context of other recent studies of semantics and the brain. The main contribution of this paper is the new method it presents for the study of semantics and the brain and the focus it permits on networks of relations detected in brain data and represented by a semantic model.</p></div

Significant structural similarities (partial orders of similarity differences invariant between the brain data and the linguistic data) for the set of words {<i>London, Moscow, Paris, north, south, east, west, Germany, Poland, Russia</i>} from 60 single-trial classifications for each participant.

<p>Significant structural similarities (partial orders of similarity differences invariant between the brain data and the linguistic data) for the set of words {<i>London, Moscow, Paris, north, south, east, west, Germany, Poland, Russia</i>} from 60 single-trial classifications for each participant.</p

Similarity scores computed using LSA for the words {<i>London, Moscow, Paris, north, south, east, west, Germany, Poland, Russia</i>} shown as a heat map.

<p>Similarity scores computed using LSA for the words {<i>London, Moscow, Paris, north, south, east, west, Germany, Poland, Russia</i>} shown as a heat map.</p