SNR estimation for clipped audio based on amplitude distribution

Liu, Xiaoqing; Jia, Jia; Cai, Lianhong

doi:10.1109/icnc.2013.6818205

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…A number of known methods for clipping detection is based on exactly this approach, and in most cases, it is proposed to use a degree of difference in the shape or parameters of the probability density function (PDF) between analyzed and undistorted signals as a measure of clipping value [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Audio Signals Clipping Detection Using Kurtosis and Its Transforms

Prodeus¹,

Didkovska²

2020

IJC

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This paper compares the results of subjective and objective assessments of the quality of speech and music signals distorted during clipping when large instantaneous signal values are replaced by a certain threshold constant or by values close to it. It was proposed in recent works to use kurtosis and some of its simple functional transforms such as reciprocal of kurtosis and square root of reciprocal of kurtosis as objective (instrumental) clipping value measures. This paper clarifies the results of a subjective assessment of the quality of speech and music signals distorted by clipping. A comparison of the obtained estimates allows one to conclude that the human auditory system is slightly more sensitive to the clipping of musical signals than to the clipping of speech signals, but this difference is small. Similarly, objective quality measures of clipped signals are almost equally sensitive to the clipping value of speech and music signals. An analysis of the variability of the kurtosis estimates, depending on the time of estimation, showed that the relative standard deviation of the kurtosis estimates is close to 10% for the analysis time interval of 1–40 s.

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Section: Introductionmentioning

confidence: 99%

“…This drawback was eliminated when the signalto-noise ratio as a measure of the clipping value was proposed to use [3]. In this publication, undistorted instantaneous values of audio signal are implied as the 'signal' while the audio signal values beyond the acceptable limits are implied as the 'noise'.…”

Section: Introductionmentioning

confidence: 99%

Audio Signals Clipping Detection Using Kurtosis and Its Transforms

Prodeus¹,

Didkovska²

2020

IJC

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“…With these considerations there are two practical signal distributions that are derived from the Laplace distribution: a long-tailed distribution that has been clipped by the ADC or associated electronics and a truncated, finite-tailed distribution that has not been clipped. In [6], clipped audio signals are examined to find their SNR with restoration attempted by estimating a parameter of a Gamma distribution.…”

Section: Introductionmentioning

confidence: 99%

Uniform Quantization of Signals with Clipped and Truncated Laplace Distributions

Bauck¹

2020

Preprint

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Many natural and man-made signals including much of speech and music are well-modeled by the Laplace distribution. Methods of synthesizing such signals are available and sometimes preferred over traditional test signals. However, sometimes those Laplace-like signals are clipped or do not exhibit infinitely-long tails. These situations are analyzed to determine their variances with an application of estimating signal-to-noise ratio as they are quantized by an analog-to-digital converter.

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