Assessing the Quality of Audio Containing Temporally Varying Distortions

Creusere, Charles D.; Hardin, Joseph

doi:10.1109/tasl.2010.2060194

Cited by 14 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the training phase, mean subjective scores and selected combinations of MOVs of the training stimuli are sent into a linear regression model: (12) where is the (number of training stimuli) (number of selected MOVs) matrix, whose elements represent the selected MOVs of the training stimuli; is a vector containing the mean subjective scores of the stimuli, and is a coefficient vector, which represents the linear weightings for the MOVs. The least-squares fitting method is used to find : (13) where the objective function is given by (14) The solution of this minimization problem is given by (15) In the evaluation phase, the predicted score of the evaluation stimulus is calculated using the selected MOVs of the stimulus and the coefficient vector obtained from the regression model: (16) where is a vector containing the selected MOVs of the evaluation stimulus.…”

Section: B Metric Design Using Movsmentioning

confidence: 99%

An Objective Analysis Method for Perceptual Quality of a Virtual Bass System

Gan

Tan

2015

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

Due to the physical size and frequency response constraints of miniaturized and flat panel loudspeakers, low frequency reproduction from these loudspeakers is generally limited and unsatisfactory. The virtual bass system (VBS) enhances the bass performance of such loudspeakers by tricking the human brain to perceive the fundamental frequency from its higher harmonics. Problematically, the additional harmonics from VBS can also introduce perceptual distortion and reduce the audio quality. Therefore, a reliable method to assess the quality of VBS-enhanced signals is necessary in the designing of VBS. Since subjective experiments are often time-consuming and may be inconsistent, it is desirable to develop an objective assessment method for VBS. Earlier studies only utilized some simple objective metrics, which generally do not consider the human auditory model and are unable to accurately predict the perceptual quality of VBS. In this paper, we introduce a perceptual quality-assessment method for VBS based on the model output variables (MOVs) of the ITU Recommendation ITU-R BS.1387. Suitable combinations of MOVs are selected to derive perceptual quality metrics that correlate closely to the subjective quality. A verification experiment is presented to justify the accuracy of the metrics.

show abstract

Section: B Metric Design Using Movsmentioning

confidence: 99%

An Objective Analysis Method for Perceptual Quality of a Virtual Bass System

Gan

Tan

2015

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

show abstract

“…For audio, Charles D. Creusere assessed the quality of audio with temporally varying distortions on the base of a subset of MOVs of PEAQ and the structural similarity measure as well as segmental signal to noise ratio [15]. A novel point of his work is that he provides estimates of audio quality varying with time.…”

Section: Recent Findingsmentioning

confidence: 99%

On objective assessment of audio quality — A review

Zheng

Zhu

2012

2012 International Conference on Audio, Language and Image Processing

View full text Add to dashboard Cite

Measurement of audio quality is of fundamental importance for numerous speech and audio processing applications, where the goal of quality assessment algorithms is to evaluate the quality in agreement with human's subjective feelings of audio quality. This paper includes a brief summary of the development of intrusive and non-intrusive audio quality assessment algorithms and outlines recent advancements, especially some new topics to be investigated presently. Moreover, according to the existing problems in this field, discussion of the future trend and challenges is also given. 1．IntroductionObjective assessment of audio quality brings a lot of convenience when ranking different sound processing equipments before launching them on the market as well as observing audio signals quality in on-line monitoring. The traditional way of audio quality assessment is to collect peoples' opinions on the quality in a wellcontrolled listening test, for example Recommendation ITU-R BS.1116 [1], which has limited applications for too expensive and time-consuming. Therefore, objective audio quality measurement is naturally desired. Early methods, like Signal-to-Noise-Ratio (S/N) or TotalHarmonic-Distortion (THD) are unable to emulate human's judgments especially when the methods are applied to non-linear and non-stationary modern codec [2]. As for these disadvantages, lots of researchers studied perceptual quality assessment and these works have led to several ITU standards. Among these standards, the International Telecommunications Union (ITU) standard for audio quality (BS.1387)[3], also called perceptual evaluation of audio quality (PEAQ) has been the only available standardized method for the purpose of audio quality assessment[4] while ITU-T P.862 (PESQ) [5] corresponding for the purpose of speech quality assessment. Then the research on objective audio quality assessment is based on the classic model of PEAQ through exploiting better psychoacoustic models or improving the cognitive algorithms to increase the accuracy of the evaluation. But the PEAQ has certain limitations, making it unsuitable for many applications. Hence more perfect and practical methods were proposed.According to the evaluation principle, the evaluation methods can be separated into two categories, intrusive and non-intrusive. The present evaluation approach mainly stays on the intrusive evaluation by measuring the "distortion" between the reference signal and degraded signal. Since intrusive methods need mapping the distortion values to the predicted quality metric, it requests both signals are strictly synchronized. However, in some cases, the reference signal may not be available (e.g. wireless communications, voice over IP network, communication systems for real-time monitoring, etc.), the objective quality assessment must be operated without the reference signal. This led to the development of non-intrusive objective audio quality assessment. Presently, no non-intrusive audio quality assessment algorithm has yet been standardized by the ITU althou...

show abstract

“…All audio test-sequences were created from three fundamentally different base-sequences sampled at a reference base quality of 44.1 kHz, with a precision of 16 bits per sample. Here, we employ the same test-sequences and distortion quality levels as in [34]. Two different types of distortions are considered for our analysis, scalar quantization and frequency band truncation, where the specific parameters are listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…Different quality levels presented to the subject during the course of an audio trial. To generate the distortion, each of these basesequences were passed through a 2048-point modified discrete cosine transform and either frequency truncation or scalar quantization was applied to the coefficients prior to reconstruction[34].…”

mentioning

confidence: 99%

Directional and Causal Information Flow in EEG for Assessing Perceived Audio Quality

Mehta

Kliewer

2017

IEEE Trans. Mol. Biol. Multi-Scale Commun.

View full text Add to dashboard Cite

In this paper, electroencephalography (EEG) measurements are used to infer change in cortical functional connectivity in response to change in audio stimulus. Experiments are conducted wherein the EEG activity of human subjects is recorded as they listen to audio sequences whose quality varies with time. A causal information theoretic framework is then proposed to measure the information flow between EEG sensors appropriately grouped into different regions of interest (ROI) over the cortex. A new causal bidirectional information (CBI) measure is defined as an improvement over standard directed information measures for the purposes of identifying connectivity between ROIs in a generalized cortical network setting. CBI can be intuitively interpreted as a causal bidirectional modification of directed information, and inherently calculates the divergence of the observed data from a multiple access channel with feedback. Further, we determine the analytical relationship between the different causal measures and compare how well they are able to distinguish between the perceived audio quality. The connectivity results inferred indicate a significant change in the rate of information flow between ROIs as the subjects listen to different audio qualities, with CBI being the best in discriminating between the perceived audio quality, compared to using standard directed information measures.

show abstract

Assessing the Quality of Audio Containing Temporally Varying Distortions

Cited by 14 publications

References 19 publications

An Objective Analysis Method for Perceptual Quality of a Virtual Bass System

An Objective Analysis Method for Perceptual Quality of a Virtual Bass System

On objective assessment of audio quality — A review

Directional and Causal Information Flow in EEG for Assessing Perceived Audio Quality

Contact Info

Product

Resources

About