Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Hidden Image Separation from Incomplete Image Mixtures by Independent Component Analysis
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Blind source separation combining frequency-domain ICA and beamforming
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Fundamental limitation of frequency domain blind source separation for convolutive mixture of speech
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Blind channel identification based on second-order statistics: a frequency-domain approach
IEEE Transactions on Information Theory
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An approach to multi-channel blind de-convolution is developed, which uses an adaptive filter that performs blind source separation in the Fourier space. The approach keeps during the learning process the same permutation and provides appropriate scaling of components for all frequency bins in the frequency space. Experiments indicate that Generalized Laplace Distribution can be used effectively to blind de-convolution of convolution mixtures of sources in Fourier space compared to the conventional Laplacian and Gaussian function.