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http://dx.doi.org/10.6109/jkiice.2013.17.5.1076

Distribution of the Slopes of Autocovariances of Speech Signals in Frequency Bands  

Kim, Seonil (거제대학교)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.17, no.5, 2013 , pp. 1076-1082 More about this Journal
Abstract
The frequency bands were discovered which maximize the slopes of autocovariances of speech signals in frequency domain to increase the possibility of segregation between speech signals and background noise signal. A speech signal is divided into blocks which include multiples of sampled data, then those blocks are transformed to frequency domain using Fast Fourier Transform(FFT). To find linear equation by Linear Regression, the coefficients of autocovariance within blocks of some frequency band are used. The slope of the linear equation which is called the slope of autocovariance is varied from band to band according to the characteristics of the speech signal. Using speech signals of a man which consist of 200 files, the coefficients of the slopes of autocovariances are analyzed and compared from band to band.
Keywords
Autocovariance; Slope; ICA; Speech Signal; Linear Regression;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 R. Johnson, K. Tsui, Statistical Reasoning and Methods, John Wiley & Sons, Inc. 1998.
2 J. F. Cardoso, "Blind signal separation: statistical principles," Proc. IEEE, vol. 9, no. 10, pp. 2009-25, Oct., 1988.
3 A. Hyvarinen and E. Oja, "Independent component analysis: algorithms and applications," Neural Networks, vol. 13, no. 4/5, pp. 411-430, 2000.   DOI   ScienceOn
4 A. Hyvarinen, "Fast and Robust Fixed-Point Algorithms for Independent Component Analysis," IEEE Trans. On Neural Networks, vol. 10, no. 2, pp. 626-634, May, 1999.   DOI   ScienceOn
5 Pl. Conon, "Independent component analysis, A new concept?," Signal Processing, vol. 36, pp. 287-314, 1994.   DOI   ScienceOn
6 W. Liu, D. Mandic, and A. Cichocki, "Analysis and Online Realization of CCA Approach for Blind Source Separation," IEEE Transaction on Neural Networks, Vol. 18, No. 3, September 2007.
7 김선일 "정준 상관 분석을 이용한 잡음 섞인 음성신호의 분리," 한국정보통신학회 종합학술대회논문집, 춘계16권, 1호, pp. 164-167, 동명대학교, 2012.   과학기술학회마을
8 H. Saruwatari, K. Sawai, T. Nishikawa, A. Lee, K. Shikano, A. Kaminuma, M. Sakata and D. Saitoh. "Speech Enhancement Based on Blind Source Separation in Car Environments," Proc. 21st International Conference on Data Engineering. pp. 1205, 05-08 April, 2005.
9 J. Lee, H, Jung, T. Lee and S. Lee, "SPEECH CODING AND NOISE REDUCTION USING ICA-BASED SPEECH FEATURES," International Workshop on independent component analysis and blind signal separation, pp. 417-422, 19-22 June, 2000, Helsinki, Finland.
10 김선일, "주파수 영역 자기 공분산 기울기를 이용한 음성과 자동차 소음 신호의 구분," 한국해양정보통신학회 논문지, 제15권, 10호, 10월, 2011.   과학기술학회마을   DOI   ScienceOn
11 김선일, "ICA로 분리한 신호의 분류," 대한전자공학회 논문지, 제47권, IE-4호, 12월, 2010.   과학기술학회마을
12 김선일, "음성 및 음성 관련 신호의 주파수 및 Quefrency 영역에서의 자기공분산 변화," 해양정보통신 종합학술대회논문집, 춘계15권, 1호, pp. 340-343, 대구 EXCO, 2011.   과학기술학회마을
13 김선일, 양성룡 "배경 잡음을 제거하는 음성 신호 잡음 제거기의 구현", 대한전자공학회 논문지, 제49권, IE-2호, pp. 24-29, 6월, 2012.   과학기술학회마을