scholarly journals Decision-directed speech power spectral density matrix estimation for multichannel speech enhancement

2017 ◽  
Vol 141 (3) ◽  
pp. EL228-EL233 ◽  
Author(s):  
Yu Gwang Jin ◽  
Jong Won Shin ◽  
Nam Soo Kim
Keyword(s):  
Density Matrix ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Speech Enhancement ◽  
Matrix Estimation ◽  
Spectral Density Matrix ◽  
Power Spectral

scholarly journals Spline-Interpolation-Based FFT Approach to Fast Simulation of Multivariate Stochastic Processes

2011 ◽  
Vol 2011 ◽  
pp. 1-24 ◽  
Author(s):  
Jinhua Li ◽  
Chunxiang Li ◽  
Shuisheng Chen
Keyword(s):  
Density Matrix ◽  
Spectral Density ◽  
Stochastic Processes ◽  
Power Spectral Density ◽  
Spline Interpolation ◽  
Spectral Density Matrix ◽  
Power Spectral ◽  
Computational Speed ◽  
Interpolation Technique ◽  
Spline Interpolation Technique

The spline-interpolation-based fast Fourier transform (FFT) algorithm, designated as the SFFT algorithm, is proposed in the present paper to further enhance the computational speed of simulating the multivariate stochastic processes. The proposed SFFT algorithm first introduces the spline interpolation technique to reduce the number of the Cholesky decomposition of a spectral density matrix and subsequently uses the FFT algorithm to further enhance the computational speed. In order to highlight the superiority of the SFFT algorithm, the simulations of the multivariate stationary longitudinal wind velocity fluctuations have been carried out, respectively, with resorting to the SFFT-based and FFT-based spectral representation SR methods, taking into consideration that the elements of cross-power spectral density matrix are the complex values. The numerical simulation results show that though introducing the spline interpolation approximation in decomposing the cross-power spectral density matrix, the SFFT algorithm can achieve the results without a loss of precision with reference to the FFT algorithm. In comparison with the FFT algorithm, the SFFT algorithm provides much higher computational efficiency. Likewise, the superiority of the SFFT algorithm is becoming more remarkable with the dividing number of frequency, the number of samples, and the time length of samples going up.

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