Performance Evaluation of The Quality of VoIP Over WLAN Codecs

The adoption of Voice over Wireless Local Area Network is on tremendous increase due its ease, non-intrusive, inexpensive deployment, low maintenance cost, universal coverage and basic roaming capabilities. However, deploying Voice over Internet Protocol (VoIP) over Wireless Local Area Network (WLAN) is a challenging task for many network managers, architects, planners, designers and engineers. Voice codec is one of the most critical components of a VoIP system. This work evaluates the effects of various codecs such as G.711, G.723.1, G.729A, G.728, G.726, Adaptive MultiRate (AMR) and Global System for Mobile communication (GSM) codecs on a VoIP over WLAN. Result from simulated network shows that the GSM codec offers the best quality of service for VoIP over WLA

Improving the robustness of CELP-like speech decoders using late-arrival packets information : application to G.729 standard in VoIP

L'utilisation de la voix sur Internet est une nouvelle tendance dans Ie secteur des télécommunications et de la réseautique. La paquetisation des données et de la voix est réalisée en utilisant Ie protocole Internet (IP). Plusieurs codecs existent pour convertir la voix codée en paquets. La voix codée est paquetisée et transmise sur Internet. À la réception, certains paquets sont soit perdus, endommages ou arrivent en retard. Ceci est cause par des contraintes telles que Ie délai («jitter»), la congestion et les erreurs de réseau. Ces contraintes dégradent la qualité de la voix. Puisque la transmission de la voix est en temps réel, Ie récepteur ne peut pas demander la retransmission de paquets perdus ou endommages car ceci va causer plus de délai. Au lieu de cela, des méthodes de récupération des paquets perdus (« concealment ») s'appliquent soit à l'émetteur soit au récepteur pour remplacer les paquets perdus ou endommages. Ce projet vise à implémenter une méthode innovatrice pour améliorer Ie temps de convergence suite a la perte de paquets au récepteur d'une application de Voix sur IP. La méthode a déjà été intégrée dans un codeur large-bande (AMR-WB) et a significativement amélioré la qualité de la voix en présence de <<jitter » dans Ie temps d'arrivée des trames au décodeur. Dans ce projet, la même méthode sera intégrée dans un codeur a bande étroite (ITU-T G.729) qui est largement utilise dans les applications de voix sur IP. Le codeur ITU-T G.729 défini des standards pour coder et décoder la voix a 8 kb/s en utilisant 1'algorithme CS-CELP (Conjugate Stmcture Algebraic Code-Excited Linear Prediction).Abstract: Voice over Internet applications is the new trend in telecommunications and networking industry today. Packetizing data/voice is done using the Internet protocol (IP). Various codecs exist to convert the raw voice data into packets. The coded and packetized speech is transmitted over the Internet. At the receiving end some packets are either lost, damaged or arrive late. This is due to constraints such as network delay (fitter), network congestion and network errors. These constraints degrade the quality of speech. Since voice transmission is in real-time, the receiver can not request the retransmission of lost or damaged packets as this will cause more delay. Instead, concealment methods are applied either at the transmitter side (coder-based) or at the receiver side (decoder-based) to replace these lost or late-arrival packets. This work attempts to implement a novel method for improving the recovery time of concealed speech The method has already been integrated in a wideband speech coder (AMR-WB) and significantly improved the quality of speech in the presence of jitter in the arrival time of speech frames at the decoder. In this work, the same method will be integrated in a narrowband speech coder (ITU-T G.729) that is widely used in VoIP applications. The ITUT G.729 coder defines the standards for coding and decoding speech at 8 kb/s using Conjugate Structure Algebraic Code-Excited Linear Prediction (CS-CELP) Algorithm

Excitação multi-taxa usando quantização vetorial estruturada em árvore para o codificador CS-ACELP com aplicação em VoIP

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.Este trabalho apresenta um estudo sobre codificação multi-taxa estruturada sobre o algoritmo CS-ACELP (Conjugate-Structure Algebraic-Code-Excited Linear-Prediction) e a especificação G.729, cujo objetivo é propor um codificador com taxa variável, através da busca da melhor excitação fixa usando codebook estruturado em árvore, para aplicações VoIP (Voice-over-IP). A mudança progressiva do transporte de voz das redes de circuito para as redes IP (Internet Protocol), apesar dos diversos aspectos positivos, tem exposto algumas deficiências intrínsecas destas, mais apropriadas ao tráfego de #melhor esforço# do que ao tráfego com requisitos de tempo. Esta proposta está inserida no conjunto das iniciativas, no âmbito do transmissor, que procuram minimizar os efeitos danosos da rede sobre a qualidade da voz reconstruída. O codebook proposto tem estrutura em árvore binária, concebida a partir de uma heurística onde os vetores CS-ACELP são ordenados por valor de forma decrescente. Uma estratégia particular de armazenamento dos nós, envolvendo simplificação nos centróides, codificação diferencial e geração automática dos dois últimos níveis da árvore, permite reduzir o espaço de armazenamento de 640 para apenas 7 kwords. Através deste modelo chega-se a 13 taxas de codificação, de 5,6 a 8,0 kbit/s, com passo de 0,2 kbit/s. A relação sinal ruído fica em 1,5 dB abaixo da mesma medida na especificação G.729 para a taxa de 5,6 kbit/s, e apenas 0,6 dB abaixo quando na taxa 8,0 kbit/s. Testes subjetivos mostraram uma qualidade bastante aceitável para a taxa mínima e praticamente indistinguível do codec original na taxa máxima. Além disso, a busca da melhor excitação é 2,4 vezes mais rápida em comparação ao codec G.729 e pode ser totalmente compatível com este se a taxa for fixa em 8,0 kbit/s. This work presents a study about multi-rate coding structured over CS-ACELP (Conjugate-Structure Algebraic-Code-Excited Linear-Prediction) algorithm and G.729 standard, whose purpose is to come up with a variable rate codec by means of best fixed excitation search using a tree structured codebook, for VoIP (Voice-over-IP) applications. The progressive change of voice transmission from circuit switched to IP (Internet orks, besides its many positive aspects, has exposed some natural deficiencies of the latter, better suited to best effort traffics than traffics with time requirements. This proposition can be inserted in the bunch of efforts, related to the sender, that seek to reduce the network impairments over the quality of reconstructed voice. The suggested codebook has a binary tree structure heuristically conceived where algebraic CSACELP vectors are disposed by value in a decreasing order. Additionally, a particular approach to store the tree nodes are considered, which involves centroid implification, differential coding and automatic generation of the last two layers of the tree, squeezing the storing space from 640 down to 7 kwords. Through this model we reach 13 coding rates, ranging from 5.6 to 8.0 kbit/s, with 0.2 kbit/s step. The signal-to-noise ratio is 1.5 dB below the same measure for G.729 standard at the rate 5.6 kbit/s, and just 0.6 dB lower at 8.0 kbit/s. Subjective tests pointed to an acceptable quality at minimum rate and virtually indistinguishable quality from the original codec at the maximum one. Also, searching for the best fixed excitation is 2.4 times faster than G.729 and can be truly compatible with it if the rate is fixed in 8 kbit/s

Speech quality prediction for voice over Internet protocol networks

Merged with duplicate record 10026.1/878 on 03.01.2017 by CS (TIS). Merged with duplicate record 10026.1/1657 on 15.03.2017 by CS (TIS)This is a digitised version of a thesis that was deposited in the University Library. If you are the author please contact PEARL Admin ([email protected]) to discuss options.IP networks are on a steep slope of innovation that will make them the long-term carrier of all types of traffic, including voice. However, such networks are not designed to support real-time voice communication because their variable characteristics (e.g. due to delay, delay variation and packet loss) lead to a deterioration in voice quality. A major challenge in such networks is how to measure or predict voice quality accurately and efficiently for QoS monitoring and/or control purposes to ensure that technical and commercial requirements are met. Voice quality can be measured using either subjective or objective methods. Subjective measurement (e.g. MOS) is the benchmark for objective methods, but it is slow, time consuming and expensive. Objective measurement can be intrusive or non-intrusive. Intrusive methods (e.g. ITU PESQ) are more accurate, but normally are unsuitable for monitoring live traffic because of the need for a reference data and to utilise the network. This makes non-intrusive methods(e.g. ITU E-model) more attractive for monitoring voice quality from IP network impairments. However, current non-intrusive methods rely on subjective tests to derive model parameters and as a result are limited and do not meet new and emerging applications. The main goal of the project is to develop novel and efficient models for non-intrusive speech quality prediction to overcome the disadvantages of current subjective-based methods and to demonstrate their usefulness in new and emerging VoIP applications. The main contributions of the thesis are fourfold: (1) a detailed understanding of the relationships between voice quality, IP network impairments (e.g. packet loss, jitter and delay) and relevant parameters associated with speech (e.g. codec type, gender and language) is provided. An understanding of the perceptual effects of these key parameters on voice quality is important as it provides a basis for the development of non-intrusive voice quality prediction models. A fundamental investigation of the impact of the parameters on perceived voice quality was carried out using the latest ITU algorithm for perceptual evaluation of speech quality, PESQ, and by exploiting the ITU E-model to obtain an objective measure of voice quality. (2) a new methodology to predict voice quality non-intrusively was developed. The method exploits the intrusive algorithm, PESQ, and a combined PESQ/E-model structure to provide a perceptually accurate prediction of both listening and conversational voice quality non-intrusively. This avoids time-consuming subjective tests and so removes one of the major obstacles in the development of models for voice quality prediction. The method is generic and as such has wide applicability in multimedia applications. Efficient regression-based models and robust artificial neural network-based learning models were developed for predicting voice quality non-intrusively for VoIP applications. (3) three applications of the new models were investigated: voice quality monitoring/prediction for real Internet VoIP traces, perceived quality driven playout buffer optimization and perceived quality driven QoS control. The neural network and regression models were both used to predict voice quality for real Internet VoIP traces based on international links. A new adaptive playout buffer and a perceptual optimization playout buffer algorithms are presented. A QoS control scheme that combines the strengths of rate-adaptive and priority marking control schemes to provide a superior QoS control in terms of measured perceived voice quality is also provided. (4) a new methodology for Internet-based subjective speech quality measurement which allows rapid assessment of voice quality for VoIP applications is proposed and assessed using both objective and traditional MOS test methods

Media gateway utilizando um GPU

Mestrado em Engenharia de Computadores e Telemátic

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

Multi-core platforms for audio and multimedia coding algorithms in telecommunications

Tietoliikenteessä käytettävät multimedian koodausalgoritmit eli koodekit kehittyvät jatkuvasti. USAC ja Opus ovat uusia, sekä puheelle että musiikille soveltuvia audiokoodekkeja. Molemmat ovat sijoittuneet korkealle koodekkien äänenlaatua vertailevissa tutkimuksissa. Näiden keskeisiä ominaisuuksia käsitellään kirjallisuuskatsaukseen perustuen. Varsinkin HD-tasoisen videon käsittelyssä käytettävät koodekit vaativat suurta laskentatehoa. Tilera TILEPro64 -moniydinsuorittimen ja sille optimoitujen multimediakoodekkien suorituskykyä testattiin tarkoitukseen suunnitelluilla tietokoneohjelmilla. Tulokset osoittivat, että suoritinytimiä lisättäessä videon koodausalgoritmien suoritusnopeus kasvaa tiettyyn rajaan asti. Testatuilla äänen koodausalgoritmeillä ytimien lisääminen ei parantanut suoritusnopeutta. Tileran moniydinratkaisuja verrattiin lopuksi Freescalen ja Texas Instrumentsin moniydinratkaisuihin. Huolimatta eroista laitteistoarkkitehtuureissa, kyseisten toimittajien kehitystyökaluissa todettiin olevan paljon samoja piirteitä.Multimedia coding algorithms used in telecommunications evolve constantly. Benefits and properties of two new hybrid audio codecs (USAC, Opus) were reviewed on a high level as a literature study. It was found that both have succeeded well in subjective sound quality measurements. Tilera TILEPro64-multicore platform and a related software library was evaluated in terms of performance in multimedia coding. The performance in video coding was found to increase with the number of processing cores up to a certain point. With the tested audio codecs, increasing the number of cores did not increase coding performance. Additionally, multicore products of Tilera, Texas Instruments and Freescale were compared. Development tools of all three vendors were found to have similar features, despite the differences in hardware architectures

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

A configurable vector processor for accelerating speech coding algorithms

The growing demand for voice-over-packer (VoIP) services and multimedia-rich applications has made increasingly important the efficient, real-time implementation of low-bit rates speech coders on embedded VLSI platforms. Such speech coders are designed to substantially reduce the bandwidth requirements thus enabling dense multichannel gateways in small form factor. This however comes at a high computational cost which mandates the use of very high performance embedded processors. This thesis investigates the potential acceleration of two major ITU-T speech coding algorithms, namely G.729A and G.723.1, through their efficient implementation on a configurable extensible vector embedded CPU architecture. New scalar and vector ISAs were introduced which resulted in up to 80% reduction in the dynamic instruction count of both workloads. These instructions were subsequently encapsulated into a parametric, hybrid SISD (scalar processor)–SIMD (vector) processor. This work presents the research and implementation of the vector datapath of this vector coprocessor which is tightly-coupled to a Sparc-V8 compliant CPU, the optimization and simulation methodologies employed and the use of Electronic System Level (ESL) techniques to rapidly design SIMD datapaths

E-model implementation for VoIP QoS across a hybrid UMTS network

Voice over Internet Protocol (VoIP) provides a new telephony approach where the voice traffic passes over Internet Protocol shared traffic networks. VoIP is a significant application of the converged network principle. The research aim is to model VoIP over a hybrid Universal Mobile Telecommunications System (UMTS) network and to identify an improved approach to applying the ITU-T Recommendation G.107 (E-Model) to understand possible Quality of Service (QoS) outcomes for the hybrid UMTS network. This research included Modeling the hybrid UMTS network and carrying out simulations of different traffic types transmitted over the network. The traffic characteristics were analysed and compared with results from the literature. VoIP traffic was modelled over the hybrid UMTS network and the VoIP traffic was generated to represent different loads on the network from light to medium and heavy VoIP traffic. The VoIP over hybrid UMTS network traffic results were characterized and used in conjunction with the E-Model to identify VoIP QoS outcomes. The E-Model technique was implemented and results achieved were compared with results for other network types highlighted in the literature. The research identified an approach that permits accurate Modeling of VoIP QoS over a hybrid UMTS network. Accurate results should allow network design to facilitate new approaches to achieving an optimal network implementation for VoIP