DETECTION OF CLIPPED FRAGMENTS IN ACOUSTIC SIGNALS

S. A. Aleynikov, Y. N. Matveev, A. V. Sholokhov


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Abstract

The paper deals with investigation of the method for detecting clipped fragments in acoustic signals with better characteristics as compared with the other known methods. This method is based on the histogram construction for the analyzed signal amplitudes and calculating the distances between the local peaks of the histogram on its tails and in the central part. The difference between histograms of non-clipped and clipped signals is that the histogram of a non-clipped signal has smoothly decaying tails while the histogram of a clipped signal has visible and easily detectable outbursts on its tails. The value of these outbursts and consequently the quality of detection of clipped fragments depends on the parameters of the method under investigation. The main aim of this paper is finding the optimal parameters of the method. Characteristics of the method are studied in detail by mathematical modeling; density functions of target values for different lengths of a studied signal frame and the number of histogram counts and levels of clipping of acoustic signals are built. It is shown that good separation between clipped and non-clipped signal fragments of acoustic signals can be achieved for the frame length between 6000 and 8000 samples and the number of histogram bins between 200 and 300. In this case the threshold level for the best separation can vary between 0.45–0.55. Examples of clipping detector operation based on the proposed method and on real acoustic signals are shown for the case of different clipping levels


Keywords: acoustic signal, clipping, clipping coefficient

References
1.     Алейник С.В., Симончик К.К. Алгоритмы выделения типовых помех и искажений в речевых сигналах // Изв. вузов. Приборостроение. 2013. Т. 56. № 2. С. 18–24.
2.     Алейник С.В., Матвеев Ю.Н., Раев А.Н. Метод оценки уровня клиппирования речевых сигналов // Научно-технический вестник информационных технологий, механики и оптики. 2012. № 3 (79). С. 79–83.
3.     Chen H., Haimovich A.M. An iterative method to restore the performance of clipped and filtered OFDM signals // IEEE International Conference on Communications. 2003. V. 5. P. 3438–3442.
4.     Zhidkov S.V. Detection of clipped code-division multiplexed signals // Electronics Letters.2005. V. 41. N 25. P. 33–34.
5.     Zillmann P., Rave W., Fettweis G. Soft detection and decoding of clipped and filtered COFDM signals // Proc. IEEE Vehicular Technology Conference. 2007. P. 1598–1602.
6.     Yang W., Ben-Zion Y. An algorithm for detecting clipped waveforms and suggested correction procedures // Seismological Research Letters. 2010. V. 81. N 1. P. 53–62.
7.     Kim J. Method and apparatus for evaluating audio distortion. Patent US 5402495, 1995.
8.     Riemer T.E., Weiss M.S., Losh M.W. Discrete clipping detection by use of a signal matched exponentially weighted differentiator //Proc. IEEE Southeastcon. New Orleans, USA, 1990. P. 245–248.
9.     Otani T., Tanaka M., Ota Y., Ito S. Clipping detection device and method. Patent US 20100030555, 2010.
10.  Liu X., Jia J., Cai L. SNR estimation for clipped audio based amplitude distribution // Proc. International Conference on Natural Computation (ICNC). Shenyang, China, 2013. P. 1434–1438.
11.  Rabiner L.R., Schafer R.W. Introduction to Digital Speech Processing. Hanover, NOWPress, 2007. 194 p.
12.  Матвеев Ю.Н. Оценка доверительного интервала общего решения ансамбля классификаторов // Изв. вузов. Приборостроение. 2013. Т. 56. № 2. С. 74–79.
13.  Матвеев Ю.Н., Симончик К.К. Система идентификации дикторов по голосу для конкурса NIST SRE 2010 // Труды 20 Международной конференции по компьютерной графике и зрению ГрафиКон'2010. Санкт-Петербург, 2010. С. 315–319.
14.  Белых И.Н., Капустин А.И., Козлов А.В., Лоханова А.И., Матвеев Ю.Н., Пеховский Т.С., Симончик К.К., Шулипа А.К. Система идентификации дикторов по голосу для конкурса NIST SRE 2010 // Информатика и ее применение. 2012. Т. 6. № 1. С. 91–98.
15.  Козлов А.В., Кудашев О.Ю., Матвеев Ю.Н., Пеховский Т.С., Симончик К.К., Шулипа А.К. Система идентификации дикторов по голосу для конкурса NIST SRE 2012 // Труды СПИИРАН. 2013. № 2 (25). С. 350–370.
16.  Kozlov A., Kudashev O., Matveev Y., Pekhovsky T., Simonchik K., Shulipa A. SVID speaker recognition system for the NIST SRE 2012 // Lecture Notes in Computer Science. 2013. V. 8113 LNAI. P. 278–285.


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