Full Text:
<3106>
CLC number: TN911
On-line Access:
Received: 2006-11-29
Revision Accepted: 2007-04-29
Crosschecked: 0000-00-00
Cited: 3
Clicked: 5653
A novel blind deconvolution algorithm using single frequency bin
| |||||||||||||
ZHANG Gui-bao, LI Jia-wen, LI Cong-xin. A novel blind deconvolution algorithm using single frequency bin[J]. Journal of Zhejiang University Science A, 2007, 8(8): 1271-1276.
@article{title="A novel blind deconvolution algorithm using single frequency bin",
author="ZHANG Gui-bao, LI Jia-wen, LI Cong-xin",
journal="Journal of Zhejiang University Science A",
volume="8",
number="8",
pages="1271-1276",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A1271"
}
%0 Journal Article
%T A novel blind deconvolution algorithm using single frequency bin
%A ZHANG Gui-bao
%A LI Jia-wen
%A LI Cong-xin
%J Journal of Zhejiang University SCIENCE A
%V 8
%N 8
%P 1271-1276
%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A1271
TY - JOUR
T1 - A novel blind deconvolution algorithm using single frequency bin
A1 - ZHANG Gui-bao
A1 - LI Jia-wen
A1 - LI Cong-xin
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 8
SP - 1271
EP - 1276
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A1271
Abstract: Former frequency-domain blind devolution algorithms need to consider a large number of frequency bins and recover the sources in different orders and with different amplitudes in each frequency bin, so they suffer from permutation and amplitude indeterminacy troubles. Based on sliding discrete Fourier transform, the presented deconvolution algorithm can directly recover time-domain sources from frequency-domain convolutive model using single frequency bin. It only needs to execute blind separation of instantaneous mixture once there are no permutation and amplitude indeterminacy troubles. Compared with former algorithms, the algorithm greatly reduces the computation cost as only one frequency bin is considered. Its good and robust performance is demonstrated by simulations when the signal-to-noise-ratio is high.
[1] Cardoso, J.F., Souloumiac, A., 1993. Blind beamforming for non-Gaussian signals. IEE Proc-F, 140:362-370.
[2] Depena, A., Serviere, C., Castedo, L., 2003. Inversion of the sliding Fourier transform using only two frequency bins and its application to source separation. Signal Processing, 83(2):453-457.
[3] Jacobsen, E., Lyons, R., 2003. The sliding DFT. IEEE Signal Processing Magazine, 20(2):74-80.
[4] Kurtia, S., Saruwatari, H., 2000. Evaluation of Blind Separation Method Using Directivity Patter under Reverberant Conditions. ICASSP 2000. Istanbul, Turkey, p.3140-3143.
[5] Mitianoudis, N., Davies, M.E., 2003. Audio source separation convolutive mixtures. IEEE Trans. on Speech and Audio Processing, 11(5):489-497.
[6] Parra, L., Spence, C., 2000. Convolutive blind source separation of non-stationary sources. IEEE Trans. on Speech and Audio Processing, 8(3):320-327.
[7] Peled, R., Braun, S., Zacksenhouse, M., 2005. A blind deconvolution separation of multiple sources, with application to bearing diagnostics. Mech. Syst. Signal Processing, 19(6):1181-1195.
[8] Smaragdis, P., 1998. Blind separation of convolved mixtures in the frequency domain. Neurocomputing, 22:21-34.
[9] Sun, X., Scott, C.D., 2001. A Natural Gradient Convolutive Blind Source Separation Algorithm for Speech Mixtures. ICA 2001. San Diego, California, USA, p.59-63.
[10] Throckmorton, C.S., Tantum, S.L., Tan, Y.Y., Collins, L.M., 2007. Independent component analysis for UXO detection in highly cluttered environments. J. Appl. Geophys., 61:304-317.
[11] Unger, H., Zeevi, Y.Y., 2006. Blind separation of spatio-temporal synfire sources and visualization of neural cliques. Neurocomputing, 69:1475-1484.
Open peer comments: Debate/Discuss/Question/Opinion
<1>