A Comparative Study on the Quality of Narrow-Band and Wide-Band AMR VoLTE Calls

This work performs a comparative analysis of the end-to-end quality guaranteed by Voice over LTE (VoLTE), examining several millions of VoLTE calls that employ two popular speech audio codecs, namely, Adaptive Multi-Rate (AMR) and Adaptive Multi-Rate Wide Band (AMR-WB). To assess call quality, VQmon, an enhanced version of the standardized E-Model, is utilized. The study reveals to what extent AMRWB based calls are more robust against network impairments than their narrowband counterparts; it further shows that the dependence of call quality on the packet loss rate is approximately exponential for both types of codec

An Effective Machine Learning (ML) Approach to Quality Assessment of Voice over IP (VoIP) Calls

This letter puts forward a supervised ML tech2 nique to determine the Quality of Experience (QoE) of VoIP calls. It takes its beginning from an investigation on VQmon, an enhanced E-model version that estimates the quality of IP-based voice calls adopting an objective approach. The current study demonstrates VQmon shortcomings via a comparison between the Mean Opinion Score (MOS) values this technique predicts and the actual average ratings collected from a subjective listening quality campaign. It proposes to deploy Ordinal Logistic Regression (OLR) for speech quality assessment, and results disclose that OLR outperforms popular ML algorithms, in accuracy and confusion matrices

Call Audio Quality Determination and Root Cause Analysis Using Machine Learning

Accurate assessment and categorization of real-world audio quality in a call, e.g., a call over VoLTE/VoNR, is essential to provide a satisfactory call experience. However, current techniques to determine call quality do not accurately categorize the audio quality. Also, there are no techniques to determine the root cause of poor audio quality or to identify potential solutions. This disclosure describes the use of machine learning clustering techniques to cluster audio metrics and using the obtained clusters to generate a root cause table. Further, a classifier is trained to determine whether an ongoing call has unsatisfactory audio quality. The quality can be categorized and labeled, e.g., good, mildly choppy, severely choppy, and no audio. If the audio quality is unsatisfactory, the likely root cause is identified using the root cause table to identify and apply solutions while the call is in progress. The described techniques are a closed loop technique to identify solutions to audio quality problems in an audio call

A methodology for obtaining More Realistic Cross-Layer QoS Measurements in mobile networks: A VoIP over LTE Use Case

Los servicios de voz han sido durante mucho tiempo la primera fuente de ingresos para los operadores móviles. Incluso con el protagonismo creciente del tráfico de datos, los servicios de voz seguirán jugando un papel importante y no desaparecerán con la transición a redes basadas en el protocolo IP. Por otra parte, hace años que los principales actores en la industria móvil detectaron claramente que los usuarios no aceptarían una degradación en la calidad de los servicios de voz. Es por esto que resulta crítico garantizar la experiencia de usuario (QoE) en la transición a redes de nueva generación basadas en conmutación de paquetes. El trabajo realizado durante esta tesis ha buscado analizar el comportamiento y las dependencias de los diferentes servicios de Voz sobre IP (VoIP), así como identificar configuraciones óptimas, mejoras potenciales y metodologías que permitan asegurar niveles de calidad aceptables al mismo tiempo que se trate de minimizar los costes. La caracterización del rendimiento del tráfico de datos en redes móviles desde el punto de vista de los usuarios finales es un proceso costoso que implica la monitorización y análisis de un amplio rango de protocolos y parámetros con complejas dependencias. Para abordar desde la raíz este problema, se requiere realizar medidas que relacionen y correlen el comportamiento de las diferentes capas. La metodología de caracterización propuesta en esta tesis proporciona la posibilidad de recoger información clave para la resolución de problemas en las comunicaciones IP, relaciolándola con efectos asociados a la propagación radio, como cambios de celda o pérdida de enlaces, o con carga de la red y limitaciones de recursos en zonas geográficas específicas. Dicha metodología se sustenta en la utilización de herramientas nativas de monitorización y registro de información en smartphones, y la aplicación de cadenas de herramientas para la experimentación extensiva tanto en redes reales y como en entornos de prueba controlados. Con los resultados proporcionados por esta serie de herramientas, tanto operadores móviles y proveedores de servicio como desarrolladores móviles podrían ganar acceso a información sobre la experiencia real del usuario y sobre cómo mejorar la cobertura, optimizar los servicios y adaptar el funcionamiento de las aplicaciones y el uso de protocolos móviles basados en IP en este contexto. Las principales contribuciones de las herramientas y métodos introducidos en esta tesis son los siguientes: - Una herramienta de monitorización multicapa para smartphones Android, llamada TestelDroid, que permite la captura de indicadores clave de rendimiento desde el propio equipo de usuario. Asimismo proporciona la capacidad de generar tráfico de forma activa y de verificar el estado de alcanzabilidad del terminal, realizando pruebas de conectividad. - Una metodología de post-procesado para correlar la información presente en las diferentes capas de las medidas realizadas. De igual forma, se proporciona la opción a los usuarios de acceder directamente a la información sobre el tráfico IP y las medidas radio y de aplicar metodologías propias para la obtención de métricas. - Se ha realizado la aplicación de la metodología y de las herramientas usando como caso de uso el estudio y evaluación del rendimiento de las comunicaciones basadas en IP a bordo de trenes de alta velocidad. - Se ha contribuido a la creación de un entorno de prueba realista y altamente configurable para la realización de experimentos avanzados sobre LTE. - Se han detectado posibles sinergias en la utilización de instrumentación avanzada de I+D en el campo de las comunicaciones móviles, tanto para la enseñanza como para la investigación en un entorno universitario

A novel non-intrusive objective method to predict voice quality of service in LTE networks.

This research aimed to introduce a novel approach for non-intrusive objective measurement of voice Quality of Service (QoS) in LTE networks. While achieving this aim, the thesis established a thorough knowledge of how voice traffic is handled in LTE networks, the LTE network architecture and its similarities and differences to its predecessors and traditional ground IP networks and most importantly those QoS affecting parameters which are exclusive to LTE environments. Mean Opinion Score (MOS) is the scoring system used to measure the QoS of voice traffic which can be measured subjectively (as originally intended). Subjective QoS measurement methods are costly and time-consuming, therefore, objective methods such as Perceptual Evaluation of Speech Quality (PESQ) were developed to address these limitations. These objective methods have a high correlation with subjective MOS scores. However, they either require individual calculation of many network parameters or have an intrusive nature that requires access to both the reference signal and the degraded signal for comparison by software. Therefore, the current objective methods are not suitable for application in real-time measurement and prediction scenarios. A major contribution of the research was identifying LTE-specific QoS affecting parameters. There is no previous work that combines these parameters to assess their impacts on QoS. The experiment was configured in a hardware in the loop environment. This configuration could serve as a platform for future research which requires simulation of voice traffic in LTE environments. The key contribution of this research is a novel non-intrusive objective method for QoS measurement and prediction using neural networks. A comparative analysis is presented that examines the performance of four neural network algorithms for non-intrusive measurement and prediction of voice quality over LTE networks. In conclusion, the Bayesian Regularization algorithm with 4 neurons in the hidden layer and sigmoid symmetric transfer function was identified as the best solution with a Mean Square Error (MSE) rate of 0.001 and regression value of 0.998 measured for the testing data set

Improved Performance of Secured VoIP Via Enhanced Blowfish Encryption Algorithm

Both the development and the integration of efficient network, open source technology, and Voice over Internet Protocol (VoIP) applications have been increasingly important and gained quick popularity due to new rapidly emerging IP-based network technology. Nonetheless, security and privacy concerns have emerged as issues that need to be addressed. The privacy process ensures that encryption and decryption methods protect the data from being alternate and intercept, a privacy VoIP call will contribute to private and confidential conversation purposes such as telebanking, telepsychiatry, health, safety issues and many more. Hence, this study had quantified VoIP performance and voice quality under security implementation with the technique of IPSec and the enhancement of the Blowfish encryption algorithm. In fact, the primary objective of this study is to improve the performance of Blowfish encryption algorithm. The proposed algorithm was tested with varying network topologies and a variety of audio codecs, which contributed to the impact upon VoIP network. A network testbed with seven experiments and network configurations had been set up in two labs to determine its effects on network performance. Besides, an experimental work using OPNET simulations under 54 experiments of network scenarios were compared with the network testbed for validation and verification purposes. Next, an enhanced Blowfish algorithm for VoIP services had been designed and executed throughout this research. From the stance of VoIP session and services performance, the redesign of the Blowfish algorithm displayed several significant effects that improved both the performance of VoIP network and the quality of voice. This finding indicates some available opportunities that could enhance encrypted algorithm, data privacy, and integrity; where the balance between Quality of Services (QoS) and security techniques can be applied to boost network throughput, performance, and voice quality of existing VoIP services. With that, this study had executed and contributed to a threefold aspect, which refers to the redesign of the Blowfish algorithm that could minimize computational resources. In addition, the VoIP network performance was analysed and compared in terms of end-to-end delay, jitter, packet loss, and finally, sought improvement for voice quality in VoIP services, as well as the effect of the designed enhanced Blowfish algorithm upon voice quality, which had been quantified by using a variety of voice codecs

Understanding the acoustic implications of digital transmission on fricatives

The aim of this thesis is to provide a better understanding of the acoustic implications of digital transmission on fricatives relevant across research fields. This is motivated by the increasing amount of digital transmitted speech across the world, and the limited knowledge on the effects of digital transmission on consonants. The thesis investigates the fricatives /f/, /θ/, /s/, /ʃ/, /z/, /ð/ and [fj]. Fricatives were expected to be particularly affected by codec compression because of their noise-like and aperiodic structure, which might be mistaken for noise by the codecs. The thesis investigates the effects of the AMR-WB-, Opus-, and MP3 codec using three different bitrates and in live transmission. The acoustic implications were measured as the first four spectral moments, peak frequency, and via spectrographic analysis. These measures were compared between baseline uncompressed WAV files and each of the codec compressed versions. This resulted in three studies. The first two are in controlled conditions i.e. the WAV files are codec compressed via a computer, whereas the third study is live with the speech transmitted between two mobile phones with and without background noise. The findings indicate significant effects of the codec compressions on the spectral measures with segment, codec and bitrate dependent tendencies. The live transmission and background noise generally produced larger effects than the controlled conditions. Intensity played a key role in the magnitude of the effects of the codec compressions and live transmission. This has implications when using codec compressed speech as data, but especially in socio- and forensic phonetics with possible diffusion of sound changes and speaker comparisons. In addition, the results have implications beyond linguistics e.g. in psychology, where clarity of speech plays a role in perceived charisma, and in hearing aid and cochlear implant technology, which both approach speech digitally and incorporate noise reduction