Performance evaluation of real-time speech through a packet network: a random neural networks-based approach

Mohamed, Samir A. Elsagheer; Rubino, Gerardo; Varela, Martín

doi:10.1016/j.peva.2003.10.007

Cited by 46 publications

(27 citation statements)

References 43 publications

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“…The work of [7] considered a bottleneck router and employed an M/M/1/k model to obtain performance metrics such as loss rate and delay (layer 2 of Figure 1) that would then be used as input to the E-model (intermediary layer of Figure 1). In this work we use the mean opinion score (MOS) as a voice quality measure and combine laboratory measurements plus simulations (layer 2 of Figure 1) as well as the random neural network (RNN) proposed in [8] and specially trained for our purposes (intermediary layer of Figure 1). A detailed discussion of the methodology to characterize the utility function is provided next.…”

Section: The Game-theoretic Modelmentioning

confidence: 99%

“…This naturally leads to a new question: how to parameterize the model? We use active measurements, NS simulations and an artificial neural network [8] to estimate the QoS perceived by the users in each of the states of the model. The key contributions of this paper are the following: (a) we extend the model proposed by [7] in order to have enough flexibility to explain the behavioral experiments; (b) we propose a methodology to infer the parameters of the model; (c) we conduct a limited number of behavioral experiments and show how the model can be used to explain their results.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Resource Sharing Dynamics of VoIP Users over a WLAN Using a Game-Theoretic Approach

Watanabe

Menasché

Silva

et al. 2008

IEEE INFOCOM 2008 - The 27th Conference on Computer Communications

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Abstract-We consider a scenario in which users share an access point and are mainly interested in VoIP applications. Each user is allowed to adapt to varying network conditions by choosing the transmission rate at which VoIP traffic is received. We denote this adaptation process by end-user congestion control, our object of study. The two questions that we ask are: (1) what are the performance consequences of letting the users to freely choose their rates? and (2) how to explain the adaptation process of the users? We set a controlled lab experiment having students as subject to answer the first question, and we extend an evolutionary game-theoretic model to address the second. Our partial answers are the following: (1) free users with local information can reach an equilibrium which is close to optimal from the system perspective. However, the equilibrium can be unfair; (2) the adaptation of the users can be explained using a game theoretic model. We propose a methodology to parameterize the later, which involves active network measurements, simulations and an artificial neural network to estimate the QoS perceived by the users in each of the states of the model.

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Section: The Game-theoretic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Resource Sharing Dynamics of VoIP Users over a WLAN Using a Game-Theoretic Approach

Watanabe

Menasché

Silva

et al. 2008

IEEE INFOCOM 2008 - The 27th Conference on Computer Communications

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show abstract

“…[36]. As more research becomes available on the issue more elaborate and accurate schemes can be employed.…”

Section: Demand Model With Respect To Price and Qualitymentioning

confidence: 99%

Demand management for telecommunications services

Zachariadis

Barria

2007

Computer Networks

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In this paper we develop a novel method of controlling the demand in a multi-class, QoS-enabled network, using pricing and resource allocation for income maximisation. We first present a solution to the problem of calculating the optimal prices and QoS for a single link using a limiting regime approximation, which reduces the associated computational burden. A heuristic algorithm is then proposed that improves the limiting regime solution, achieving better results for links with small capacity. We further extend this approach to a multi-link network, where a distributed iterative algorithm is developed based on the solution of the single link model. Results from small and medium size networks show that, even when the assumptions we used do not hold, our approach yields results very close to the optimal ones (0.17% to 2.95% difference), which are computed by exhaustively searching in the decision space. Moreover, the calculation time using the proposed approach is approximately 1.5 minutes for problems which took more than 240 minutes to solve using exhaustive search.

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“…The approach of [3] to replace PESQ takes its roots from the idea of Pseudo-Subjective Quality Assessment (PSQA), a technique based on merging subjective quality assessment with methods of statistical learning [11], [12]. The idea is to observe that if quality degrades, this comes from the impact of many network impairments such as losses or delays, plus factors related to the application itself (codec used, bandwidth of the connection, etc.).…”

Section: Introductionmentioning

confidence: 99%

“…A kind of distance between the signals is computed using a cognitive model, to evaluate the perceived quality of the degraded signal [10]. The results are usually presented in the form of a Mean Opinion Score (MOS) (coming from its use in subjective testing), using a scale between 1 (Bad) and 5 (Excellent).The approach of [3] to replace PESQ takes its roots from the idea of Pseudo-Subjective Quality Assessment (PSQA), a technique based on merging subjective quality assessment with methods of statistical learning [11], [12]. The idea is to observe that if quality degrades, this comes from the impact of many network impairments such as losses or delays, plus factors related to the application itself (codec used, bandwidth of the connection, etc.).…”

mentioning

confidence: 99%

Real-Time Estimation of Speech Quality through the Internet Using Echo State Networks

Basterrech¹,

Rubino²

2013

JACN

Self Cite

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Abstract-Audio quality in the Internet can be strongly affected by network conditions. As a consequence, many techniques to evaluate it have been developed. In particular, the ITU-T adopted in 2001 a technique called Perceptual Evaluation of Speech Quality (PESQ) to automatically measuring speech quality. PESQ is a well-known and widely used procedure, providing in general an accurate evaluation of perceptual quality by comparing the original and received voice sequences.One obvious inherent limitation of PESQ is, thus, that it requires the original signal (we say the reference), to make its evaluation. This precludes the use of PESQ for assessing the perceived quality in real-time, as the reference is in general not available.In this paper, we describe a procedure for estimating PESQ output working only with measures taken on the network state and properties of the communication system, without any use of the reference. It is based on the use of statistical learning techniques. Specifically, we rely on recent ideas for learning with specific types of neural networks, known under the name of Echo State Networks (ESNs), a member of the class of Reservoir Computing systems. These tools have been proven to be very efficient and robust in many learning tasks. The experimental results obtained show the good accuracy of the resulting procedure, and its capability to give its estimations of speech quality in a real-time context. This allows putting our measuring modules in future Internet applications or services based on voice transmission, for instance for control purposes.Index Terms-Quality assessment, speech quality, echo state networks, reservoir computing. I. INTRODUCTIONMeasuring the quality of a voice signal transmitted over the Internet is an important topic today, and one of main available tools for this purpose is the Perceptual Evaluation of Speech Quality (PESQ) method accepted in 2001 as the ITU-T objective speech quality measurement standard P.862 [1]. The network conditions vary over time, and in many contexts, several different factors lead to losses, which in turn lead to degradations in the perception of the quality by the users. PESQ analyzes this quality by comparing the received signal with the original speech sequence. For this reason, we say that it is a "full reference" technique, the reference being the original signal. Researchers in many areas use PESQ and the tool has been widely diffused in commercial measurement products. Recently, the ITU started to update its voice Manuscript received March 1, 2013; revised April 15, 2013. This work was supported in part by the European Celtic Project "QuEEN".S. Basterrech is with the University of Rennes 2, Rennes, France (e-mail: Sebastian.Basterrechtiscordio@etudiant.uhb.fr).G. Rubino is with the National Institute for Research in Computer Science and Control (INRIA Rennes -Bretagne Atlantique), Rennes, France (e-mail: Gerardo.Rubino@inria.fr). assessment recommendations by promoting the new P.863 standard Perceptual Objective Listening Quality Asse...

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Performance evaluation of real-time speech through a packet network: a random neural networks-based approach

Cited by 46 publications

References 43 publications

Modeling Resource Sharing Dynamics of VoIP Users over a WLAN Using a Game-Theoretic Approach

Modeling Resource Sharing Dynamics of VoIP Users over a WLAN Using a Game-Theoretic Approach

Demand management for telecommunications services

Real-Time Estimation of Speech Quality through the Internet Using Echo State Networks

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