Convolutive blind separation of non-white broadband signals based on a double-iteration method

Authors:
Hua Zhang;Dazhang Feng
Affiliations:
National Laboratory of Radar Signal Processing, Xidian University, Xi’an, P.R. China;National Laboratory of Radar Signal Processing, Xidian University, Xi’an, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 4
Cited 0

Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
An efficient multistage decomposition approach for independent components

Signal Processing
Inversion of the sliding Fourier transform using only two frequency bins and its application to source separation

Signal Processing
Blind source separation based on time-frequency signalrepresentations

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a convolutive blind source separation (BSS) algorithm based on a double-iteration method is proposed to process the convolutive mixed non-white broadband signals. By sliding Fourier transform (SFT), the convolutive mixture problem is changed into instantaneous case in time-frequent domain, which can be solved by applying an instantaneous separation method for every frequent bin. A novel cost function for each frequent bin based on joint diagonalization of a set of correlation matrices with multiple time-lags is constructed. Through combination of the proposed double-iteration method with a restriction on the length of inverse filter in time domain, the inverse of transfer channel or separation matrix, which has consistent permutations for all frequencies, can be estimated. Then it is easy to calculate the recovered source signals. The results of simulations also illustrate the algorithm has not only fast convergence performance, but also higher recovered accuracy and output SER (Signal to Error of reconstruction Ratio).