Blind Identification and Signal Separation of Two-InputTwo-Output FIR Systems
Multidimensional Systems and Signal Processing
On-line Convolutive Blind Source Separation of Non-Stationary Signals
Journal of VLSI Signal Processing Systems
Applying Neural Networks and Genetic Algorithms to the Separation of Sources
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Blind source separation in frequency domain
Signal Processing
Blind separation of convolutive mixtures by decorrelation
Signal Processing
Linear State-Space Models for Blind Source Separation
The Journal of Machine Learning Research
Exploiting acoustic similarity of propagating paths for audio signal separation
EURASIP Journal on Applied Signal Processing
Blind separation of convolutive image mixtures
Neurocomputing
An Independent Component Analysis Evolution Based Method for Nonlinear Speech Processing
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
An approach to surface EMG decomposition based on higher-order cumulants
Computer Methods and Programs in Biomedicine
Blind signal separation and identification of mixtures of images
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Real-time independent vector analysis for convolutive blind source separation
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Real and imaginary modulation spectral subtraction for speech enhancement
Speech Communication
| Hi-index | 35.68 |
The problem of multichannel signal separation has attracted considerable interest in recent literature. A variety of methods and criteria have been proposed to solve the problem based on statistical independence between the source signals. Most of these criteria involve the computation of high-order statistics of the observed signals. We present a unified framework for many of these criteria and analyze their statistical variance