Analysis of speech signals’ characteristics based on MF-DFA with moving overlapping windows
暂无分享,去创建一个
[1] Masahiro Nakagawa,et al. The fractal properties of vocal sounds and their application in the speech recognition model , 1996 .
[2] R. Ellis. The bluefin in peril. , 2008, Scientific American.
[3] B. Mandelbrot. A Multifractal Walk down Wall Street , 1999 .
[4] Xiongwei Zhang,et al. Research on Speaker Recognition Based on Multifractal Spectrum Feature , 2010, 2010 Second International Conference on Computer Modeling and Simulation.
[7] Witold Kinsner,et al. Speech segmentation using multifractal measures and amplification of signal features , 2008, 2008 7th IEEE International Conference on Cognitive Informatics.
[8] Witold Kinsner,et al. Consonant characterization using correlation fractal dimension for speech recognition , 1995, IEEE WESCANEX 95. Communications, Power, and Computing. Conference Proceedings.
[9] Wang Conghui. Speech emotion recognition based on multifractal , 2012 .
[10] H. Stanley,et al. Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series , 2002, physics/0202070.
[11] Rosângela Coelho,et al. Text-independent speaker recognition based on the Hurst parameter and the multidimensional fractional Brownian motion model , 2006, IEEE Transactions on Audio, Speech, and Language Processing.
[12] Petros Maragos,et al. Fractal aspects of speech signals: dimension and interpolation , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.
[13] Wei-Xing Zhou,et al. Detrending moving average algorithm for multifractals. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.
[14] Diana Cristina González,et al. Analysis of the Multifractal Nature of Speech Signals , 2012, CIARP.