2020
DOI: 10.1121/10.0002113
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Spectral distortion level resulting in a just-noticeable difference between an a priori signal-to-noise ratio estimate and its instantaneous case

Abstract: Minimum mean-square error (MMSE) approaches to speech enhancement are widely used in the literature. The quality of enhanced speech produced by an MMSE approach is directly impacted by the accuracy of the employed a priori signal-to-noise ratio (SNR) estimator. In this paper, the a priori SNR estimate spectral distortion (SD) level that results in a just-noticeable difference (JND) in the perceived quality of MMSE approach enhanced speech is found. The JND SD level is indicative of the accuracy that an a prior… Show more

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Cited by 4 publications
(2 citation statements)
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“…where C represents the lift coefficient, D represents the diameter of the tennis ball, and the meaning of other parameters remains unchanged. Under the influence of the above noise value, predict the state vector of the tennis ball and take the above calculated noise value as a priori estimation value, and the state vector can be expressed as [18,19]…”
Section: Parabolic Trajectory Detection Of Tennis Servementioning
confidence: 99%
“…where C represents the lift coefficient, D represents the diameter of the tennis ball, and the meaning of other parameters remains unchanged. Under the influence of the above noise value, predict the state vector of the tennis ball and take the above calculated noise value as a priori estimation value, and the state vector can be expressed as [18,19]…”
Section: Parabolic Trajectory Detection Of Tennis Servementioning
confidence: 99%
“…where r represents the feature contrast measure, σ c represents the range of feature area, and σ s represents the range of film, television, and animation video image suppression area. F(c) represents the coarse-scale film and television animation video image, which can suppress the features other than the target area [35]. F(s) represents the fine-scale film and television animation video image, which can describe the detail features of the target area, and then the scale difference between F(c) and F(s) is shown in the following equation:…”
Section: Frame Feature Extraction Of Video Image Under Graymentioning
confidence: 99%