• Arkadiy Prodeus
  • Maryna Didkovska



speech clipping, music clipping, clipping detection, clipping value measure, kurtosis


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.


T. Otani, M. Tanaka, Y. Ota, S. Ito, Clipping detection device and method, Patent US 8,392,199 B2, Int. Cl. G10 19/00, 2013.

G.R. Avanesyan, Method and device for estimating and indicating distortions of output signal of audio frequency amplifiers (overload indication), Patent RU 2274868 C2, Int. Cl. G01R 23/20, G01R 19/165, 2006.

X. Liu, J. Jia, L. Cai, “SNR estimation for clipped audio based on amplitude distribution,” Proc. of the IEEE 9th Int. Conf. on Natural Computation (ICNC), Shenyang, China, 23-25 July, 2013, pp. 1434-1438.

S.V. Aleinik, Yu.N. Matveev, and A.N. Rayev, “Evaluation method of speech signal clipping level,” Scientific and Technical Journal of Information Technologies, Mechanics and Optics, no. 3 (79), pp. 79–83, 2012.

S.V. Aleinik, Yu.N. Matveev, and A.V. Sholokhov, “Detection of clipped fragments in acoustic signals,” International Journal of Computer and Information Engineering, vol. 8, no. 2, pp. 286-292, 2014.

F. Bie, D. Wang, J. Wang, T.F. Zheng “Detection and reconstruction of clipped speech for speaker recognition,” Speech Communication, vol. 72, pp. 218-231, September 2015.

C. Laguna, A. Lerch, “An efficient algorithm for clipping detection and declipping audio,” Proc. of the AES 141st Convention, September 29-October 2, Los Angeles, USA, 2016, 10 p.

M. Kendall, A. Stuart, The Advanced Theory of Statistics: Distribution theory, London, Wiley, 1977, 700 p.

A. Prodeus, I. Kotvytskyi, A. Ditiashov, “Assessment of clipped speech quality,” Electronics and Control Systems, no. 4(58), pp. 11-18, 2018.

A. Prodeus, I. Kotvytskyi, A. Grebin, “Using kurtosis for objective assessment of the musical signals clipping degree,” Proc. of the 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T), October 2019, Kyiv, Ukraine, pp. 655-659.

J. Moors, “The Meaning of Kurtosis: Darlington Reexamined,” The American Statistician, 40:4, pp. 283-284, 1986.

V. Arora and R. Kumar, “Probability distribution estimation of music signals in time and frequency,” Proc. of the IEEE 19th Int. Conf. on Digital Signal Processing (DSP-2014), August 2014, Hong Kong, pp. 409-414.

N. Cote, Integral and diagnostic intrusive prediction of speech, Springer-Verlag: Berlin-Heidelberg, 2011, 250 p.

A. Poorjamm J. Jensen, M. Little, M. Christensen, “Dominant Distortion Classification for Pre-Processing of Vowels in Remote Biomedical Voice Analysis,” INTERSPEECH 2017, August 20-24, Stockholm, Sweden, 2017.

R. Dwyer, “FRAM II single channel ambient noise statistics,” Proc. of the 101st Meet. Acoustical Soc. of America, May 19, 1981, Published in NUSC Tech. Doc. 6588, 25 November 1981.

R. Dwyer, “Use of the kurtosis statistic in the frequency domain as an aid in detecting random signals,” IEEE Journal of Oceanic Engineering, vol. OE-9, no. 2, pp. 85-92, April 1984.

V. Vrabie, P. Granjon, and C. Servière, “Spectral kurtosis: from definition to application,” Proc. of the 6th IEEE-EURASIP International Workshop on Nonlinear Signal and Image Processing, Grado, Italy, 2003, pp. 1-5.

J. Antoni, “The spectral kurtosis: a useful tool for characterizing non-stationary signals,” Mech. Syst. Signal Pr., vol. 20, pp. 282–307, 2006.

S. Naida, “Acoustic theory problems of speech production in the light of the discovery of the formula for the middle ear norm parameter,” Proc. of IEEE 35th Int. Sc. Conf. on Electronics and Nanotechnology (ELNANO), 21-24 April, Kyiv, Ukraine, 2015, pp. 347-350.

S. Naida, N. Naida, V. Didkovskyi, O. Pavlenko, “Spectral Analysis of Sounds by Acoustic Hearing Analyzer,” Proceedings of IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO), April 16-18, Kyiv, Ukraine, 2019, pp. 421-424.

S. Lunova, O. Pedchenko, I. Rudenko, “Speech spectrum of the Ukrainian language,” Proc. of the IEEE 39th Int. Conf. on Electronics and Nanotechnology (ELNANO), April 16-18, Kyiv, Ukraine, 2019, pp. 444-448.




How to Cite

Prodeus, A., & Didkovska, M. (2020). AUDIO SIGNALS CLIPPING DETECTION USING KURTOSIS AND ITS TRANSFORMS. International Journal of Computing, 19(3), 411-417.