Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
A fast fixed-point algorithm for independent component analysis
Neural Computation
The symmetric eigenvalue problem
The symmetric eigenvalue problem
Blind source separation using the maximum signal fraction approach
Signal Processing
Blind Source Separation Using a Matrix Pencil
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Blind source separation via generalized eigenvalue decomposition
The Journal of Machine Learning Research
Blind Source Separation Using Temporal Predictability
Neural Computation
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
A two-stage algorithm for MIMO blind deconvolution of nonstationarycolored signals
IEEE Transactions on Signal Processing
A matrix-pencil approach to blind separation of colorednonstationary signals
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Nonlinear blind source separation using kernels
IEEE Transactions on Neural Networks
On Optimal Selection of Correlation Matrices for Matrix-Pencil-Based Separation
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
| Hi-index | 0.00 |
This paper proposes a novel formulation of the generalized eigendecomposition (GED) approach to blind source separation (BSS) problems. The generalized eigendecomposition algorithms consider the estimation of a pair of correlation matrices (a matrix pencil) using observed sensor signals. Each of various algorithms proposed in the literature uses a different approach to form the pencil. This study proposes a linear algebra formulation which exploits the definition of congruent matrix pencils and shows that the solution and its constraints are independent of the way the matrix pencil is computed. Also an iterative eigendecomposition algorithm, that updates separation parameters on a sample-by-sample basis, is developed. It comprises of: (1) performing standard eigendecompositions based on power and deflation techniques; (2) computing a transformation matrix using spectral factorization. Another issue discussed in this work is the influence of the length of the data segment used to estimate the pencil. The algorithm is applied to artificially mixed audio data and it is shown that the separation performance depends on the eigenvalue spread. The latter varies with the number of samples used to estimate the eigenvalues.