Une architecture modulaire pour l'extraction de caractéristiques en reconnaissance de phonèmes

- Dans ce papier, nous présentons une architecture appelée Modular Neural Prédictive Coding (MNPC). Elle est utilisée pour l'extraction de caractéristiques discriminantes. Cette architecture est conçue à l'aide de connaissances phonétiques. On estime les performances de cette architecture sur une tâche de reconnaissance de phonèmes extraits de la base Darpa-Timit. Une comparaison avec les méthodes de codage (LPC, MFCC et PLP) montrent une nette amélioration du taux de reconnaissance

Generalised Mutual Information: a Framework for Discriminative Clustering

In the last decade, recent successes in deep clustering majorly involved the Mutual Information (MI) as an unsupervised objective for training neural networks with increasing regularisations. While the quality of the regularisations have been largely discussed for improvements, little attention has been dedicated to the relevance of MI as a clustering objective. In this paper, we first highlight how the maximisation of MI does not lead to satisfying clusters. We identified the Kullback-Leibler divergence as the main reason of this behaviour. Hence, we generalise the mutual information by changing its core distance, introducing the Generalised Mutual Information (GEMINI): a set of metrics for unsupervised neural network training. Unlike MI, some GEMINIs do not require regularisations when training as they are geometry-aware thanks to distances or kernels in the data space. Finally, we highlight that GEMINIs can automatically select a relevant number of clusters, a property that has been little studied in deep discriminative clustering context where the number of clusters is a priori unknown.Comment: Submitted for review at the IEEE Transactions on Pattern Analysis and Machine Intelligence. This article is an extension of an original NeurIPS 2022 article [arXiv:2210.06300

Unsupervised video indexing on audiovisual characterization of persons

Cette thèse consiste à proposer une méthode de caractérisation non-supervisée des intervenants dans les documents audiovisuels, en exploitant des données liées à leur apparence physique et à leur voix. De manière générale, les méthodes d'identification automatique, que ce soit en vidéo ou en audio, nécessitent une quantité importante de connaissances a priori sur le contenu. Dans ce travail, le but est d'étudier les deux modes de façon corrélée et d'exploiter leur propriété respective de manière collaborative et robuste, afin de produire un résultat fiable aussi indépendant que possible de toute connaissance a priori. Plus particulièrement, nous avons étudié les caractéristiques du flux audio et nous avons proposé plusieurs méthodes pour la segmentation et le regroupement en locuteurs que nous avons évaluées dans le cadre d'une campagne d'évaluation. Ensuite, nous avons mené une étude approfondie sur les descripteurs visuels (visage, costume) qui nous ont servis à proposer de nouvelles approches pour la détection, le suivi et le regroupement des personnes. Enfin, le travail s'est focalisé sur la fusion des données audio et vidéo en proposant une approche basée sur le calcul d'une matrice de cooccurrence qui nous a permis d'établir une association entre l'index audio et l'index vidéo et d'effectuer leur correction. Nous pouvons ainsi produire un modèle audiovisuel dynamique des intervenants.This thesis consists to propose a method for an unsupervised characterization of persons within audiovisual documents, by exploring the data related for their physical appearance and their voice. From a general manner, the automatic recognition methods, either in video or audio, need a huge amount of a priori knowledge about their content. In this work, the goal is to study the two modes in a correlated way and to explore their properties in a collaborative and robust way, in order to produce a reliable result as independent as possible from any a priori knowledge. More particularly, we have studied the characteristics of the audio stream and we have proposed many methods for speaker segmentation and clustering and that we have evaluated in a french competition. Then, we have carried a deep study on visual descriptors (face, clothing) that helped us to propose novel approches for detecting, tracking, and clustering of people within the document. Finally, the work was focused on the audiovisual fusion by proposing a method based on computing the cooccurrence matrix that allowed us to establish an association between audio and video indexes, and to correct them. That will enable us to produce a dynamic audiovisual model for each speaker

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

From habitat to management: a simulation framework for improving statistical methods in fisheries science

224 p.Monte Carlo simulation consists in computer experiments that involve creating data by pseudo-random sampling and has shown to be a powerful tool for studying the performance of statistical methods. In this thesis Monte Carlo simulation was used to improve statistical methodology related to three different fields of fisheries science: 1) Species distribution models (SDM) field, where focusing on regression-based models, we proposed using shape-constrained generalised additive models (SC-GAMs) to build SDMs in agreement with the ecological niche theory imposing concavity constraints in the linear predictor scale and testing their performance trough Monte Carlos simulation, 2) stock assessment models field, where uncertainty estimation methods for statistical catch-at-age models with non-parametric effects on fishing mortality were compared through simulation in addition to the comparison of two available stock assessment models to an ad-hoc Bayesian approach, and 3) management advice field, where a full-feedback management strategy evaluation (MSE) is developed for the sardine in the Bay of Biscay, incorporating the official Stoch Synthesis assessment model within the Monte Carlo simulation, and introducing gradually different sources of uncertainty such as process, parameter and observation error in order to study their effect in management advice. Monte Carlo simulation was an adequate tool to accomplish the objectives of this thesis that definitely could not have been achieved using only available real data or analytical solutions

From habitat to management: a simulation framework for improving statistical methods in fisheries science

Monte Carlo simulation consists of computer experiments that involve creating data by pseudo-random sampling and has shown to be a powerful tool for studying the performance of statistical methods. In this thesis Monte Carlo simulation was used to improve statistical methodology related to three different fields of fisheries science: 1) Species distribution models (SDM) field, where focusing on regression-based models, we proposed using shape-constrained generalised additive models (SC-GAMs) to build SDMs in agreement with the ecological niche theory imposing concavity constraints in the linear predictor scale and testing their performance trough Monte Carlo simulation, 2) stock assessment models field, where uncertainty estimation methods for statistical catch-at-age models with non-parametric effects on fishing mortality were compared through simulation in addition to the comparison of two available stock assessment models to an ad-hoc Bayesian approach, and 3) management advice field, where a full-feedback management strategy evaluation (MSE) was developed for the sardine in the Bay of Biscay, incorporating the official Stoch Synthesis assessment model within the Monte Carlo simulation, and introducing gradually different sources of uncertainty such as process, parameter and observation error in order to study their effect in management advice. Monte Carlo simulation was an adequate tool to accomplish the objectives of this thesis that definitely could not have been achieved using only available real data or analytical solutions