Objective Assessment of Speech and Audio Quality—Technology and Applications

Rix, Antony W.; Beerends, John G.; Kim, Doh-Suk; Kroon, P.; Ghitza, Oded

doi:10.1109/tasl.2006.883260

Cited by 105 publications

(85 citation statements)

References 35 publications

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“…As the performance of a machine learning algorithm is influenced by the quality and quantity of training data [55], the development of a large corpus of human labeled training data is an important issue. However, the use of human subjects for labeling data for a particular application such as speech quality is an expensive and time consuming task [13]. The solution we propose is to use an intrusive objective technique to label large amounts of training data automatically.…”

Section: Classification and Regression Treementioning

confidence: 99%

“…In PESQ, quality scores are determined on a scale from -0.5 to 4.5 and a mapping function is then used to map the PESQ score to MOS on the ACR scale [12]. A correlation coefficient of 0.935 between PESQ and MOS has been reported for a number of telecommunication relevant databases [13], [8]. More recently, an extension of PESQ has been standardized as POLQA [14].…”

Section: Introductionmentioning

confidence: 99%

“…A number of nonintrusive techniques have been proposed over the past decade, see [13], [3] for a review. The current ITU-T industry standard algorithm for non-intrusive speech quality assessment is P.563 [15], which uses a number of features from the audio stream to estimate the quality score directly on the MOS scale.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A data-driven non-intrusive measure of speech quality and intelligibility

Sharma

Wang

Naylor

et al. 2016

Speech Communication

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Section: Classification and Regression Treementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A data-driven non-intrusive measure of speech quality and intelligibility

Sharma

Wang

Naylor

et al. 2016

Speech Communication

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“…[10,11] 의 EBU의 주관 평가법에 대한 요약 및 Bech와 Zacharov [33] 의 주관 평가 이론, 방법론 그리고 응용에 재한 상세한 정리가 있다. 객관 평가법 리뷰에는 Rix 등, [34] Campbell 등 [35] 의 연구가 있으며, 종합적인 리 뷰에는 de Lima 등 …”

Section: 음질 평가법의 표준과 연구 동향 -전송 처리음 분야unclassified

Review of Standard Sound Quality Assessment Methods for the Transmitted and Processed Sounds

Oh¹

2013

The Journal of the Acoustical Society of Korea

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Assessing the quality of audio signals is an important consideration in making high quality sounds and various methods have been developed. This paper provides a general framework of sound quality and a technical overview of the international standard methods which are described in ITU-T, ITU-R, IEC and ANSI Recommendations in the speech intelligibility, speech quality, and audio quality areas. In addition, some recent findings and future works are included.

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“…Packets can be lost due to routing problems, network congestion, delays at intermediate points in the network (making packets arrive too late), etc. Among these, the loss process is in some sense the main affecting component with respect to the perceived quality [13], [14]: if everything arrives (and arrives in time), the receiver perceives exactly what is sent (assuming the speech signal perfect at the sender's side). In this paper, we focus on this loss process.…”

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confidence: 99%

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

Basterrech¹,

Rubino²

2013

JACN

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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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Objective Assessment of Speech and Audio Quality—Technology and Applications

Cited by 105 publications

References 35 publications

A data-driven non-intrusive measure of speech quality and intelligibility

A data-driven non-intrusive measure of speech quality and intelligibility

Review of Standard Sound Quality Assessment Methods for the Transmitted and Processed Sounds

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

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