DocumentCode :
3519660
Title :
Joint estimation of signal and noise correlation matrices and its application to inverse filtering
Author :
Tanaka, Akira ; Miyakoshi, Masaaki
Author_Institution :
Div. of Comput. Sci., Hokkaido Univ., Sapporo
fYear :
2009
fDate :
19-24 April 2009
Firstpage :
2181
Lastpage :
2184
Abstract :
Noise suppression by linear filters for a time series is discussed. We propose a method for jointly estimating signal and noise correlation matrices by incorporating steering vectors of the noise or eigenvectors of the noise correlation matrix as well as steering vectors of the target signals. Our estimates bring us two significant advantages. One is reduction of computational cost in obtaining the Wiener filter since the Wiener post filter, which is combined to the minimum variance distortionless response filter (MVDRF), is no longer needed with the estimates of signal and noise correlation matrices. The other is an improvement of the performance of the MVDRF since we can construct the regularized version of it with an estimate of the noise correlation matrix.
Keywords :
Wiener filters; correlation methods; filtering theory; matrix algebra; signal denoising; MVDRF; Wiener filter; eigenvector; linear filter; minimum variance distortionless response filter; noise correlation matrix; noise suppression; signal estimation; steering vector; time series; Acoustic distortion; Acoustic noise; Application software; Computational efficiency; Computer science; Filtering; Noise generators; Nonlinear filters; Vectors; Wiener filter; Wiener filter; correlation matrix; linear filter; noise suppression;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location :
Taipei
ISSN :
1520-6149
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2009.4960050
Filename :
4960050
Link To Document :
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