a b s t r a c tThe increasing consumption of multimedia services and the demand of high-quality services from customers has triggered a fundamental change in how we administer networks in terms of abstraction, separation, and mapping of forwarding, control and management aspects of services. The industry and the academia are embracing 5G as the future network capable to support next generation vertical applications with different service requirements. To realize this vision in 5G network, the physical network has to be sliced into multiple isolated logical networks of varying sizes and structures which are dedicated to different types of services based on their requirements with different characteristics and requirements (e.g., a slice for massive IoT devices, smartphones or autonomous cars, etc.). Softwarization using Software-Defined Networking (SDN) and Network Function Virtualization (NFV)in 5G networks are expected to fill the void of programmable control and management of network resources.In this paper, we provide a comprehensive review and updated solutions related to 5G network slicing using SDN and NFV. Firstly, we present 5G service quality and business requirements followed by a description of 5G network softwarization and slicing paradigms including essential concepts, history and different use cases. Secondly, we provide a tutorial of 5G network slicing technology enablers including SDN, NFV, MEC, cloud/Fog computing, network hypervisors, virtual machines & containers. Thidly, we comprehensively survey different industrial initiatives and projects that are pushing forward the adoption of SDN and NFV in accelerating 5G network slicing. A comparison of various 5G architectural approaches in terms of practical implementations, technology adoptions and deployment strategies is presented. Moreover, we provide a discussion on various open source orchestrators and proof of concepts representing industrial contribution. The work also investigates the standardization efforts in 5G networks regarding network slicing and softwarization. Additionally, the article presents the management and orchestration of network slices in a single domain followed by a comprehensive survey of management and orchestration approaches in 5G network slicing across multiple domains while supporting multiple tenants. Furthermore, we highlight the future challenges and research directions regarding network softwarization and slicing using SDN and NFV in 5G networks.Crown (A .A . Barakabitze). tems [1,2,3] . The increasing number of smart devices (e.g., tablets and smartphones) and the growing number of bandwidth-hungry mobile applications (e.g., live video streaming, online video gaming) which demand higher spectral efficiency than that of 4G systems are posing significant challenges in 5G. The Cisco Visual Networking Index (VNI) Forecast [4] predicts that IP video traffic will be 82% of all consumer Internet traffic by 2022, up from 75% in 2017. Mobile video traffic alone will account for 78% of the global mobile data traffic. W...

This paper presents an objective speech quality model, ViSQOL, the Virtual Speech Quality Objective Listener. It is a signal-based, full-reference, intrusive metric that models human speech quality perception using a spectro-temporal measure of similarity between a reference and a test speech signal. The metric has been particularly designed to be robust for quality issues associated with Voice over IP (VoIP) transmission. This paper describes the algorithm and compares the quality predictions with the ITU-T standard metrics PESQ and POLQA for common problems in VoIP: clock drift, associated time warping, and playout delays. The results indicate that ViSQOL and POLQA significantly outperform PESQ, with ViSQOL competing well with POLQA. An extensive benchmarking against PESQ, POLQA, and simpler distance metrics using three speech corpora (NOIZEUS and E4 and the ITU-T P.Sup. 23 database) is also presented. These experiments benchmark the performance for a wide range of quality impairments, including VoIP degradations, a variety of background noise types, speech enhancement methods, and SNR levels. The results and subsequent analysis show that both ViSQOL and POLQA have some performance weaknesses and under-predict perceived quality in certain VoIP conditions. Both have a wider application and robustness to conditions than PESQ or more trivial distance metrics. ViSQOL is shown to offer a useful alternative to POLQA in predicting speech quality in VoIP scenarios.

Abstract-There are many types of degradation which can occur in Voice over IP calls. Degradations which occur independently of the codec, hardware, or network in use are the focus of this paper. The development of new quality metrics for modern communication systems depends heavily on the availability of suitable test and development data with subjective quality scores. A new dataset of VoIP degradations (TCD-VoIP) has been created and is presented in this paper. The dataset contains speech samples with a range of common VoIP degradations, and the corresponding set of subjective opinion scores from 24 listeners. The dataset is publicly available.

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Streaming services seek to optimise their use of bandwidth across audio and visual channels to maximise the quality of experience for users. This letter evaluates whether objective quality metrics can predict the audio quality for music encoded at low bitrates by comparing objective predictions with results from listener tests. Three objective metrics were benchmarked: PEAQ, POLQA, and VISQOLAudio. The results demonstrate objective metrics designed for speech quality assessment have a strong potential for quality assessment of low bitrate audio codecs.

Estimation of perceptual quality in audio and speech is possible using a variety of methods. The combined v3 release of ViSQOL and ViSQOLAudio (for speech and audio, respectively,) provides improvements upon previous versions, in terms of both design and usage. As an open source C++ library or binary with permissive licensing, ViSQOL can now be deployed beyond the research context into production usage. The feedback from internal production teams at Google has helped to improve this new release, and serves to show cases where it is most applicable, as well as to highlight limitations. The new model is benchmarked against real-world data for evaluation purposes. The trends and direction of future work is discussed.

Hearing loss research has traditionally been based on perceptual criteria, speech intelligibility and threshold levels. The development of computational models of the auditory-periphery has allowed experimentation via simulation to provide quantitative, repeatable results at a more granular level than would be practical with clinical research on human subjects. The responses of the model used in this study have been previously shown to be consistent with a wide range of physiological data from both normal and impaired ears for stimuli presentation levels spanning the dynamic range of hearing.The model output can be assessed by examination of the spectro-temporal output visualised as neurograms. The effect of sensorineural hearing loss (SNHL) on phonemic structure was evaluated in this study using two types of neurograms: temporal fine structure (TFS) and average discharge rate or temporal envelope. A new systematic way of assessing phonemic degradation is proposed using the outputs of an auditory nerve model for a range of SNHLs. The mean structured similarity index (MSSIM) is an objective measure originally developed to assess perceptual image quality. The measure is adapted here for use in measuring the phonemic degradation in neurograms derived from impaired auditory nerve outputs. A full evaluation of the choice of parameters for the metric is presented using a large amount of natural human speech.The metric's boundedness and the results for TFS neurograms indicate it is a superior metric to standard point to point metrics of relative mean absolute error and relative mean squared error. MSSIM as an indicative score of intelligibility is also promising, with results similar to those of the standard Speech Intelligibility Index metric.

Digital audio broadcasting services transmit substantial amounts of data that is encoded to minimize bandwidth whilst maximizing user quality of experience. Many large service providers continually alter codecs to improve the encoding process. Performing subjective tests to validate each codec alteration would be impractical, necessitating the use of objective perceptual audio quality models. This paper evaluates the quality scores from ViSQOLAudio, an objective perceptual audio quality model, against the quality scores of PEAQ, POLQA, and PEMO-Q on three datasets containing fullband audio encoded with a variety of codecs and bitrates. The results show that ViSQOLAudio was more accurate than all other models on two of the datasets and performed well on the third, demonstrating the utility of ViSQOLAudio for predicting the perceptual audio quality for encoded music.Index Terms-Perceived audio quality, subjective audio quality assessment, objective audio quality assessment, ViSQOLAudio, ViSQOL, POLQA, PEAQ, PEMO-Q.