Détection et modification des transitoires d'un signal de parole dans le but de rendre un codec plus robuste aux pertes de paquets

Pour transmettre les signaux de parole de façon efficace, ces derniers sont compressés et transmis en trames typiquement de 10 à 20 ms. Lors de la transmission des trames, il arrive que ces dernières soient perdues. Lors de la reconstruction du signal au décodeur, il est préférable de remplacer les trames perdues par un signal qui se rapproche le plus possible du signal manquant. Le signal perdu est souvent reconstruit en se basant sur l'information des dernières trames reçues, puisque, de façon générale, les propriétés statistiques du signal de parole évoluent relativement lentement d'une trame à la suivante. Les signaux de parole peuvent être classés en différentes catégories (parole voisée, nonvoisée, transitoire, etc.). Afin de mieux exploiter les caractéristiques de chaque catégorie, il est pertinent d'appliquer un classificateur à chaque trame de signal. Cette classification des signaux permet un meilleur camouflage des trames perdues, optimisé pour les différentes classes. La classification des trames est parfois imprécise lors des transitions entre une trame non-voisée et une trame voisée. Ces erreurs de classification entraînent de mauvaises reconstructions de signal lors des pertes de trames. Pour pallier ces erreurs, cette Thèse propose un nouvel algorithme robuste qui identifie les trames critiques et qui applique la classification appropriée. Pour les trames dont les propriétés ne correspondent pas exactement à l'une des classes disponibles, une modification transparente du signal est appliquée pour rendre ces trames conformes à la classification proposée. Ces modifications permettent d'obtenir une meilleure reconstruction du signal si les trames suivantes sont perdues

On Predictive Coding for Erasure Channels Using a Kalman Framework

We present a new design method for robust low-delay coding of autoregressive (AR) sources for transmission across erasure channels. It is a fundamental rethinking of existing concepts. It considers the encoder a mechanism that produces signal measurements from which the decoder estimates the original signal. The method is based on linear predictive coding and Kalman estimation at the decoder. We employ a novel encoder state-space representation with a linear quantization noise model. The encoder is represented by the Kalman measurement at the decoder. The presented method designs the encoder and decoder offline through an iterative algorithm based on closed-form minimization of the trace of the decoder state error covariance. The design method is shown to provide considerable performance gains, when the transmitted quantized prediction errors are subject to loss, in terms of signal-to-noise ratio (SNR) compared to the same coding framework optimized for no loss. The design method applies to stationary auto-regressive sources of any order. We demonstrate the method in a framework based on a generalized differential pulse code modulation (DPCM) encoder. The presented principles can be applied to more complicated coding systems that incorporate predictive coding as well

A General Framework for Analyzing, Characterizing, and Implementing Spectrally Modulated, Spectrally Encoded Signals

Fourth generation (4G) communications will support many capabilities while providing universal, high speed access. One potential enabler for these capabilities is software defined radio (SDR). When controlled by cognitive radio (CR) principles, the required waveform diversity is achieved via a synergistic union called CR-based SDR. Research is rapidly progressing in SDR hardware and software venues, but current CR-based SDR research lacks the theoretical foundation and analytic framework to permit efficient implementation. This limitation is addressed here by introducing a general framework for analyzing, characterizing, and implementing spectrally modulated, spectrally encoded (SMSE) signals within CR-based SDR architectures. Given orthogonal frequency division multiplexing (OFDM) is a 4G candidate signal, OFDM-based signals are collectively classified as SMSE since modulation and encoding are spectrally applied. The proposed framework provides analytic commonality and unification of SMSE signals. Applicability is first shown for candidate 4G signals, and resultant analytic expressions agree with published results. Implementability is then demonstrated in multiple coexistence scenarios via modeling and simulation to reinforce practical utility

Apprentissage automatique pour le codage cognitif de la parole

Depuis les années 80, les codecs vocaux reposent sur des stratégies de codage à court terme qui fonctionnent au niveau de la sous-trame ou de la trame (généralement 5 à 20 ms). Les chercheurs ont essentiellement ajusté et combiné un nombre limité de technologies disponibles (transformation, prédiction linéaire, quantification) et de stratégies (suivi de forme d'onde, mise en forme du bruit) pour construire des architectures de codage de plus en plus complexes. Dans cette thèse, plutôt que de s'appuyer sur des stratégies de codage à court terme, nous développons un cadre alternatif pour la compression de la parole en codant les attributs de la parole qui sont des caractéristiques perceptuellement importantes des signaux vocaux. Afin d'atteindre cet objectif, nous résolvons trois problèmes de complexité croissante, à savoir la classification, la prédiction et l'apprentissage des représentations. La classification est un élément courant dans les conceptions de codecs modernes. Dans un premier temps, nous concevons un classifieur pour identifier les émotions, qui sont parmi les attributs à long terme les plus complexes de la parole. Dans une deuxième étape, nous concevons un prédicteur d'échantillon de parole, qui est un autre élément commun dans les conceptions de codecs modernes, pour mettre en évidence les avantages du traitement du signal de parole à long terme et non linéaire. Ensuite, nous explorons les variables latentes, un espace de représentations de la parole, pour coder les attributs de la parole à court et à long terme. Enfin, nous proposons un réseau décodeur pour synthétiser les signaux de parole à partir de ces représentations, ce qui constitue notre dernière étape vers la construction d'une méthode complète de compression de la parole basée sur l'apprentissage automatique de bout en bout. Bien que chaque étape de développement proposée dans cette thèse puisse faire partie d'un codec à elle seule, chaque étape fournit également des informations et une base pour la prochaine étape de développement jusqu'à ce qu'un codec entièrement basé sur l'apprentissage automatique soit atteint. Les deux premières étapes, la classification et la prédiction, fournissent de nouveaux outils qui pourraient remplacer et améliorer des éléments des codecs existants. Dans la première étape, nous utilisons une combinaison de modèle source-filtre et de machine à état liquide (LSM), pour démontrer que les caractéristiques liées aux émotions peuvent être facilement extraites et classées à l'aide d'un simple classificateur. Dans la deuxième étape, un seul réseau de bout en bout utilisant une longue mémoire à court terme (LSTM) est utilisé pour produire des trames vocales avec une qualité subjective élevée pour les applications de masquage de perte de paquets (PLC). Dans les dernières étapes, nous nous appuyons sur les résultats des étapes précédentes pour concevoir un codec entièrement basé sur l'apprentissage automatique. un réseau d'encodage, formulé à l'aide d'un réseau neuronal profond (DNN) et entraîné sur plusieurs bases de données publiques, extrait et encode les représentations de la parole en utilisant la prédiction dans un espace latent. Une approche d'apprentissage non supervisé basée sur plusieurs principes de cognition est proposée pour extraire des représentations à partir de trames de parole courtes et longues en utilisant l'information mutuelle et la perte contrastive. La capacité de ces représentations apprises à capturer divers attributs de la parole à court et à long terme est démontrée. Enfin, une structure de décodage est proposée pour synthétiser des signaux de parole à partir de ces représentations. L'entraînement contradictoire est utilisé comme une approximation des mesures subjectives de la qualité de la parole afin de synthétiser des échantillons de parole à consonance naturelle. La haute qualité perceptuelle de la parole synthétisée ainsi obtenue prouve que les représentations extraites sont efficaces pour préserver toutes sortes d'attributs de la parole et donc qu'une méthode de compression complète est démontrée avec l'approche proposée.Abstract: Since the 80s, speech codecs have relied on short-term coding strategies that operate at the subframe or frame level (typically 5 to 20ms). Researchers essentially adjusted and combined a limited number of available technologies (transform, linear prediction, quantization) and strategies (waveform matching, noise shaping) to build increasingly complex coding architectures. In this thesis, rather than relying on short-term coding strategies, we develop an alternative framework for speech compression by encoding speech attributes that are perceptually important characteristics of speech signals. In order to achieve this objective, we solve three problems of increasing complexity, namely classification, prediction and representation learning. Classification is a common element in modern codec designs. In a first step, we design a classifier to identify emotions, which are among the most complex long-term speech attributes. In a second step, we design a speech sample predictor, which is another common element in modern codec designs, to highlight the benefits of long-term and non-linear speech signal processing. Then, we explore latent variables, a space of speech representations, to encode both short-term and long-term speech attributes. Lastly, we propose a decoder network to synthesize speech signals from these representations, which constitutes our final step towards building a complete, end-to-end machine-learning based speech compression method. The first two steps, classification and prediction, provide new tools that could replace and improve elements of existing codecs. In the first step, we use a combination of source-filter model and liquid state machine (LSM), to demonstrate that features related to emotions can be easily extracted and classified using a simple classifier. In the second step, a single end-to-end network using long short-term memory (LSTM) is shown to produce speech frames with high subjective quality for packet loss concealment (PLC) applications. In the last steps, we build upon the results of previous steps to design a fully machine learning-based codec. An encoder network, formulated using a deep neural network (DNN) and trained on multiple public databases, extracts and encodes speech representations using prediction in a latent space. An unsupervised learning approach based on several principles of cognition is proposed to extract representations from both short and long frames of data using mutual information and contrastive loss. The ability of these learned representations to capture various short- and long-term speech attributes is demonstrated. Finally, a decoder structure is proposed to synthesize speech signals from these representations. Adversarial training is used as an approximation to subjective speech quality measures in order to synthesize natural-sounding speech samples. The high perceptual quality of synthesized speech thus achieved proves that the extracted representations are efficient at preserving all sorts of speech attributes and therefore that a complete compression method is demonstrated with the proposed approach

The development of speech coding and the first standard coder for public mobile telephony

This thesis describes in its core chapter (Chapter 4) the original algorithmic and design features of the ??rst coder for public mobile telephony, the GSM full-rate speech coder, as standardized in 1988. It has never been described in so much detail as presented here. The coder is put in a historical perspective by two preceding chapters on the history of speech production models and the development of speech coding techniques until the mid 1980s, respectively. In the epilogue a brief review is given of later developments in speech coding. The introductory Chapter 1 starts with some preliminaries. It is de- ??ned what speech coding is and the reader is introduced to speech coding standards and the standardization institutes which set them. Then, the attributes of a speech coder playing a role in standardization are explained. Subsequently, several applications of speech coders - including mobile telephony - will be discussed and the state of the art in speech coding will be illustrated on the basis of some worldwide recognized standards. Chapter 2 starts with a summary of the features of speech signals and their source, the human speech organ. Then, historical models of speech production which form the basis of di??erent kinds of modern speech coders are discussed. Starting with a review of ancient mechanical models, we will arrive at the electrical source-??lter model of the 1930s. Subsequently, the acoustic-tube models as they arose in the 1950s and 1960s are discussed. Finally the 1970s are reviewed which brought the discrete-time ??lter model on the basis of linear prediction. In a unique way the logical sequencing of these models is exposed, and the links are discussed. Whereas the historical models are discussed in a narrative style, the acoustic tube models and the linear prediction tech nique as applied to speech, are subject to more mathematical analysis in order to create a sound basis for the treatise of Chapter 4. This trend continues in Chapter 3, whenever instrumental in completing that basis. In Chapter 3 the reader is taken by the hand on a guided tour through time during which successive speech coding methods pass in review. In an original way special attention is paid to the evolutionary aspect. Speci??cally, for each newly proposed method it is discussed what it added to the known techniques of the time. After presenting the relevant predecessors starting with Pulse Code Modulation (PCM) and the early vocoders of the 1930s, we will arrive at Residual-Excited Linear Predictive (RELP) coders, Analysis-by-Synthesis systems and Regular- Pulse Excitation in 1984. The latter forms the basis of the GSM full-rate coder. In Chapter 4, which constitutes the core of this thesis, explicit forms of Multi-Pulse Excited (MPE) and Regular-Pulse Excited (RPE) analysis-by-synthesis coding systems are developed. Starting from current pulse-amplitude computation methods in 1984, which included solving sets of equations (typically of order 10-16) two hundred times a second, several explicit-form designs are considered by which solving sets of equations in real time is avoided. Then, the design of a speci??c explicitform RPE coder and an associated eÆcient architecture are described. The explicit forms and the resulting architectural features have never been published in so much detail as presented here. Implementation of such a codec enabled real-time operation on a state-of-the-art singlechip digital signal processor of the time. This coder, at a bit rate of 13 kbit/s, has been selected as the Full-Rate GSM standard in 1988. Its performance is recapitulated. Chapter 5 is an epilogue brie y reviewing the major developments in speech coding technology after 1988. Many speech coding standards have been set, for mobile telephony as well as for other applications, since then. The chapter is concluded by an outlook

Methodologies to improve product life cycle decision making in the telecommunications industry

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.As pressure from regulation and customers increases on telecommunications equipment manufacturers and service providers to reduce the hazardous material content of telecommunications products and generally improve environmental performance, new methods for Product Life Cycle Management are required. Supplier and component environmental evaluation are vital and fundamental elements of any Product Life Cycle Management programme, as is the capture of data from the supply base. The information that needs to be captured from the supply base to meet the requirements of customers of telecommunications equipment providers; to meet the requirements of legislation; and to provide data for improving ecodesign and facilitating product-focused continual improvement for ISO 14001 has been identified. A method for capturing data from the supply base has been developed and recommendations made for implementation. A hierarchical supplier and component eco-evaluation methodology has been developed and tested. This methodology incorporates supplier environmental management performance, component inherent human toxicity, ecotoxicity and resource depletion. It provides component qualifiers and purchasers with a method of supplier environmental performance comparison and enables this criterion to be integrated with existing criteria such as quality and cost in the component and supplier selection decision-making process. Recommendations are made regarding the implementation of an industry-wide system to enable the capture of detailed product material composition data from the supply chain and the implementation of the eco-evaluation methodology to identify the supplier that has superior environmental performance. The result will be enhanced decision making in product design and manufacture, improved transparency in communication to customers and more informed decision-making at the end-of-life stage of the product life cycle.Funding was obtained from the Engineering and Physical Sciences Research Council

Methodologies to improve product life cycle decision making in the telecommunications industry

As pressure from regulation and customers increases on telecommunications equipment manufacturers and service providers to reduce the hazardous material content of telecommunications products and generally improve environmental performance, new methods for Product Life Cycle Management are required. Supplier and component environmental evaluation are vital and fundamental elements of any Product Life Cycle Management programme, as is the capture of data from the supply base. The information that needs to be captured from the supply base to meet the requirements of customers of telecommunications equipment providers; to meet the requirements of legislation; and to provide data for improving ecodesign and facilitating product-focused continual improvement for ISO 14001 has been identified. A method for capturing data from the supply base has been developed and recommendations made for implementation. A hierarchical supplier and component eco-evaluation methodology has been developed and tested. This methodology incorporates supplier environmental management performance, component inherent human toxicity, ecotoxicity and resource depletion. It provides component qualifiers and purchasers with a method of supplier environmental performance comparison and enables this criterion to be integrated with existing criteria such as quality and cost in the component and supplier selection decision-making process. Recommendations are made regarding the implementation of an industry-wide system to enable the capture of detailed product material composition data from the supply chain and the implementation of the eco-evaluation methodology to identify the supplier that has superior environmental performance. The result will be enhanced decision making in product design and manufacture, improved transparency in communication to customers and more informed decision-making at the end-of-life stage of the product life cycle.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research CouncilGBUnited Kingdo

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Massachusetts Domestic and Foreign Corporations Subject to an Excise: For the Use of Assessors (2004)

International audienc