18,087 research outputs found
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
Quality aspects of Internet telephony
Internet telephony has had a tremendous impact on how people communicate.
Many now maintain contact using some form of Internet telephony.
Therefore the motivation for this work has been to address the quality aspects
of real-world Internet telephony for both fixed and wireless telecommunication.
The focus has been on the quality aspects of voice communication,
since poor quality leads often to user dissatisfaction. The scope of the work
has been broad in order to address the main factors within IP-based voice
communication.
The first four chapters of this dissertation constitute the background
material. The first chapter outlines where Internet telephony is deployed
today. It also motivates the topics and techniques used in this research.
The second chapter provides the background on Internet telephony including
signalling, speech coding and voice Internetworking. The third chapter
focuses solely on quality measures for packetised voice systems and finally
the fourth chapter is devoted to the history of voice research.
The appendix of this dissertation constitutes the research contributions.
It includes an examination of the access network, focusing on how calls are
multiplexed in wired and wireless systems. Subsequently in the wireless
case, we consider how to handover calls from 802.11 networks to the cellular
infrastructure. We then consider the Internet backbone where most of our
work is devoted to measurements specifically for Internet telephony. The
applications of these measurements have been estimating telephony arrival
processes, measuring call quality, and quantifying the trend in Internet telephony
quality over several years. We also consider the end systems, since
they are responsible for reconstructing a voice stream given loss and delay
constraints. Finally we estimate voice quality using the ITU proposal PESQ
and the packet loss process.
The main contribution of this work is a systematic examination of Internet
telephony. We describe several methods to enable adaptable solutions
for maintaining consistent voice quality. We have also found that relatively
small technical changes can lead to substantial user quality improvements.
A second contribution of this work is a suite of software tools designed to
ascertain voice quality in IP networks. Some of these tools are in use within
commercial systems today
Measuring Instruction in Higher Education: Summary of a Convening
What will it take to improve the quality of instruction in higher education? An important first step is the ability to measure quality. A variety of measurement systems exist, but how informative are they, and how can we bring greater coherence to instructional measurement in higher education?On November 17 -- 18, 2014, the William T. Grant Foundation, the Spencer Foundation, and the Bill & Melinda Gates Foundation sponsored a convening of experts on education and the learning sciences to address these questions and to guide possible future initiatives by the foundations.The report examines incentive structures in colleges and universities, looks at the goals toward which instructional measurement can be directed, describes past and current research on instructional measurement, and summarizes potential future initiatives
09192 Abstracts Collection -- From Quality of Service to Quality of Experience
From 05.05. to 08.05.2009, the Dagstuhl Seminar 09192 ``From Quality of Service to Quality of Experience\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
From QoS Distributions to QoE Distributions: a System's Perspective
In the context of QoE management, network and service providers commonly rely
on models that map system QoS conditions (e.g., system response time, paket
loss, etc.) to estimated end user QoE values. Observable QoS conditions in the
system may be assumed to follow a certain distribution, meaning that different
end users will experience different conditions. On the other hand, drawing from
the results of subjective user studies, we know that user diversity leads to
distributions of user scores for any given test conditions (in this case
referring to the QoS parameters of interest). Our previous studies have shown
that to correctly derive various QoE metrics (e.g., Mean Opinion Score (MOS),
quantiles, probability of users rating "good or better", etc.) in a system
under given conditions, there is a need to consider rating distributions
obtained from user studies, which are often times not available. In this paper
we extend these findings to show how to approximate user rating distributions
given a QoS-to-MOS mapping function and second order statistics. Such a user
rating distribution may then be combined with a QoS distribution observed in a
system to finally derive corresponding distributions of QoE scores. We provide
two examples to illustrate this process: 1) analytical results using a Web QoE
model relating waiting times to QoE, and 2) numerical results using
measurements relating packet losses to video stall pattern, which are in turn
mapped to QoE estimates. The results in this paper provide a solution to the
problem of understanding the QoE distribution in a system, in cases where the
necessary data is not directly available in the form of models going beyond the
MOS, or where the full details of subjective experiments are not available.Comment: 4th International Workshop on Quality of Experience Management (QoE
Management 2020), featured by IEEE Conference on Network Softwarization (IEEE
NetSoft 2020), Ghent, Belgiu
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