Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Identification of multichannel MA parameters using higher-order statistics
Signal Processing - Special issue on higher order statistics
A sequential subspace method for blind identification of general FIR MIMO channels
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Signal Processing
Convolutive Blind Signal Separation Based on Asymmetrical Contrast Functions
IEEE Transactions on Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Subspace methods for the blind identification of multichannel FIRfilters
IEEE Transactions on Signal Processing
A two-stage algorithm for MIMO blind deconvolution of nonstationarycolored signals
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
On blind multiuser forward link channel estimation by the subspacemethod: identifiability results
IEEE Transactions on Signal Processing
Blind MIMO system identification based on cumulant subspace decomposition
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Convolutive Blind Source Separation in the Frequency Domain Based on Sparse Representation
IEEE Transactions on Audio, Speech, and Language Processing
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
A subspace algorithm for certain blind identification problems
IEEE Transactions on Information Theory
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
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This paper takes a close look at the block Toeplitz structure and block-inner diagonal structure of auto correlation matrices of source signals in convolutive blind source separation (BSS) problems. The aim is to propose a one-stage time-domain algorithm for convolutive BSS by explicitly exploiting the structure in autocorrelation matrices of source signals at different time delays and inherent relations among these matrices. The main idea behind the proposed algorithm is to implement the joint block Toeplitzation and block-inner diagonalization (JBTBID) of a set of correlation matrices of the observed vector sequence such that the mixture matrix can be extracted. For this purpose, a novel tri-quadratic cost function is introduced. The important feature of this tri-quadratic contrast function enables the development of an efficient algebraic method based on triple iterations for searching the minimum point of the cost function, which is called the triply iterative algorithm (TIA). Through the cyclic minimization process in the proposed TIA, it is expected that the JBTBID is achieved. The source signals can be retrieved. Moreover, the asymptotic convergence of the proposed TIA is analyzed. Convergence performance of the TIA and the separation results are also demonstrated by simulations in comparison with some other prominent two-stage time-domain methods.