Aeronautical audio broadcasting via satellite

A system design for aeronautical audio broadcasting, with C-band uplink and L-band downlink, via Inmarsat space segments is presented. Near-transparent-quality compression of 5-kHz bandwidth audio at 20.5 kbit/s is achieved based on a hybrid technique employing linear predictive modeling and transform-domain residual quantization. Concatenated Reed-Solomon/convolutional codes with quadrature phase shift keying are selected for bandwidth and power efficiency. RF bandwidth at 25 kHz per channel, and a decoded bit error rate at 10(exp -6) with E(sub b)/N(sub o) at 3.75 dB are obtained. An interleaver, scrambler, modem synchronization, and frame format were designed, and frequency-division multiple access was selected over code-division multiple access. A link budget computation based on a worst-case scenario indicates sufficient system power margins. Transponder occupancy analysis for 72 audio channels demonstrates ample remaining capacity to accommodate emerging aeronautical services

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

Slight-Delay Shaped Variable Bit Rate (SD-SVBR) Technique for Video Transmission

The aim of this thesis is to present a new shaped Variable Bit Rate (VBR) for video transmission, which plays a crucial role in delivering video traffic over the Internet. This is due to the surge of video media applications over the Internet and the video typically has the characteristic of a highly bursty traffic, which leads to the Internet bandwidth fluctuation. This new shaped algorithm, referred to as Slight Delay - Shaped Variable Bit Rate (SD-SVBR), is aimed at controlling the video rate for video application transmission. It is designed based on the Shaped VBR (SVBR) algorithm and was implemented in the Network Simulator 2 (ns-2). SVBR algorithm is devised for real-time video applications and it has several limitations and weaknesses due to its embedded estimation or prediction processes. SVBR faces several problems, such as the occurrence of unwanted sharp decrease in data rate, buffer overflow, the existence of a low data rate, and the generation of a cyclical negative fluctuation. The new algorithm is capable of producing a high data rate and at the same time a better quantization parameter (QP) stability video sequence. In addition, the data rate is shaped efficiently to prevent unwanted sharp increment or decrement, and to avoid buffer overflow. To achieve the aim, SD-SVBR has three strategies, which are processing the next Group of Picture (GoP) video sequence and obtaining the QP-to-data rate list, dimensioning the data rate to a higher utilization of the leaky-bucket, and implementing a QP smoothing method by carefully measuring the effects of following the previous QP value. However, this algorithm has to be combined with a network feedback algorithm to produce a better overall video rate control. A combination of several video clips, which consisted of a varied video rate, has been used for the purpose of evaluating SD-SVBR performance. The results showed that SD-SVBR gains an impressive overall Peak Signal-to-Noise Ratio (PSNR) value. In addition, in almost all cases, it gains a high video rate but without buffer overflow, utilizes the buffer well, and interestingly, it is still able to obtain smoother QP fluctuation

Scalable Speech Coding for IP Networks

The emergence of Voice over Internet Protocol (VoIP) has posed new challenges to the development of speech codecs. The key issue of transporting real-time voice packet over IP networks is the lack of guarantee for reasonable speech quality due to packet delay or loss. Most of the widely used narrowband codecs depend on the Code Excited Linear Prediction (CELP) coding technique. The CELP technique utilizes the long-term prediction across the frame boundaries and therefore causes error propagation in the case of packet loss and need to transmit redundant information in order to mitigate the problem. The internet Low Bit-rate Codec (iLBC) employs the frame-independent coding and therefore inherently possesses high robustness to packet loss. However, the original iLBC lacks in some of the key features of speech codecs for IP networks: Rate flexibility, Scalability, and Wideband support. This dissertation presents novel scalable narrowband and wideband speech codecs for IP networks using the frame independent coding scheme based on the iLBC. The rate flexibility is added to the iLBC by employing the discrete cosine transform (DCT) and iii the scalable algebraic vector quantization (AVQ) and by allocating different number of bits to the AVQ. The bit-rate scalability is obtained by adding the enhancement layer to the core layer of the multi-rate iLBC. The enhancement layer encodes the weighted iLBC coding error in the modified DCT (MDCT) domain. The proposed wideband codec employs the bandwidth extension technique to extend the capabilities of existing narrowband codecs to provide wideband coding functionality. The wavelet transform is also used to further enhance the performance of the proposed codec. The performance evaluation results show that the proposed codec provides high robustness to packet loss and achieves equivalent or higher speech quality than state-of-the-art codecs under the clean channel condition

Hybrid video quality prediction: reviewing video quality measurement for widening application scope

A tremendous number of objective video quality measurement algorithms have been developed during the last two decades. Most of them either measure a very limited aspect of the perceived video quality or they measure broad ranges of quality with limited prediction accuracy. This paper lists several perceptual artifacts that may be computationally measured in an isolated algorithm and some of the modeling approaches that have been proposed to predict the resulting quality from those algorithms. These algorithms usually have a very limited application scope but have been verified carefully. The paper continues with a review of some standardized and well-known video quality measurement algorithms that are meant for a wide range of applications, thus have a larger scope. Their individual artifacts prediction accuracy is usually lower but some of them were validated to perform sufficiently well for standardization. Several difficulties and shortcomings in developing a general purpose model with high prediction performance are identified such as a common objective quality scale or the behavior of individual indicators when confronted with stimuli that are out of their prediction scope. The paper concludes with a systematic framework approach to tackle the development of a hybrid video quality measurement in a joint research collaboration.Polish National Centre for Research and Development (NCRD) SP/I/1/77065/10, Swedish Governmental Agency for Innovation Systems (Vinnova

Survey of error concealment schemes for real-time audio transmission systems

This thesis presents an overview of the main strategies employed for error detection and error concealment in different real-time transmission systems for digital audio. The “Adaptive Differential Pulse-Code Modulation (ADPCM)”, the “Audio Processing Technology Apt-x100”, the “Extended Adaptive Multi-Rate Wideband (AMR-WB+)”, the “Advanced Audio Coding (AAC)”, the “MPEG-1 Audio Layer II (MP2)”, the “MPEG-1 Audio Layer III (MP3)” and finally the “Adaptive Transform Coder 3 (AC3)” are considered. As an example of error management, a simulation of the AMR-WB+ codec is included. The simulation allows an evaluation of the mechanisms included in the codec definition and enables also an evaluation of the different bit error sensitivities of the encoded audio payload.Ingeniería Técnica en Telemátic