Noise removing for time-variant vocal signal by generalized modulation

Since the instantaneous frequencies of vocal signals always vary with time, it is inconvenient to use the conventional filter to remove the noise of vocal signals. In this paper, we propose a method that uses the generalized modulation to reshape and minimize the areas of the spectrograms of vocal signals. Instead of multiplying an exponential function with the first order phase, the generalized modulation is to multiply an exponential function whose phase is a higher order polynomial. With the proposed noise-removing algorithm based on generalized modulation, the signal part and the noise part of a vocal signal can be well separated and the effect of noise can be significantly reduced.

[1]  A. W. M. van den Enden,et al.  Discrete Time Signal Processing , 1989 .

[2]  Soo-Chang Pei,et al.  Fractional Fourier Transform, Wigner Distribution, and Filter Design for Stationary and Nonstationary Random Processes , 2010, IEEE Transactions on Signal Processing.

[3]  M. R. Spiegel Mathematical handbook of formulas and tables , 1968 .

[4]  Soo-Chang Pei,et al.  Relations Between Gabor Transforms and Fractional Fourier Transforms and Their Applications for Signal Processing , 2006, IEEE Transactions on Signal Processing.

[5]  P. K. Chaturvedi,et al.  Communication Systems , 2002, IFIP — The International Federation for Information Processing.

[6]  Levent Onural,et al.  Optimal filtering in fractional Fourier domains , 1997, IEEE Trans. Signal Process..

[7]  D Mendlovic,et al.  Fractional Wiener filter. , 1996, Applied optics.

[8]  P. Laguna,et al.  Signal Processing , 2002, Yearbook of Medical Informatics.

[9]  B. A. Shenoi,et al.  Introduction to Digital Signal Processing and Filter Design , 2005 .

[10]  Chien-Cheng Tseng,et al.  Discrete fractional Fourier transform based on orthogonal projections , 1999, IEEE Trans. Signal Process..

[11]  Rainer Martin,et al.  Noise power spectral density estimation based on optimal smoothing and minimum statistics , 2001, IEEE Trans. Speech Audio Process..

[12]  Soo-Chang Pei,et al.  Higher order modulation and the efficient sampling algorithm for time variant signal , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[13]  S. Boll,et al.  Suppression of acoustic noise in speech using spectral subtraction , 1979 .

[14]  M. Bastiaans,et al.  Gabor's expansion of a signal into Gaussian elementary signals , 1980, Proceedings of the IEEE.

[15]  Z. Zalevsky,et al.  The Fractional Fourier Transform: with Applications in Optics and Signal Processing , 2001 .

[16]  Jacob Benesty,et al.  New insights into the noise reduction Wiener filter , 2006, IEEE Transactions on Audio, Speech, and Language Processing.