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
Multichannel Algorithms for Simultaneous Equalization andInterference Suppression
Wireless Personal Communications: An International Journal
Separating Convolutive Mixtures by Mutual Information Minimization
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Convolutive blind separation of speech mixtures using the natural gradient
Speech Communication - Special issue on speech processing for hearing aids
Blind separation of convolutive mixtures by decorrelation
Signal Processing
Exploiting acoustic similarity of propagating paths for audio signal separation
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
Adaptive IQ channel matching for quadrature IF receivers
ESPOCO'05 Proceedings of the 4th WSEAS International Conference on Electronic, Signal Processing and Control
Adaptive IQ mismatch cancellation for quadrature if receivers
ISPRA'05 Proceedings of the 4th WSEAS International Conference on Signal Processing, Robotics and Automation
A new second-order method for blind signal separation from dynamic mixtures
Computers and Electrical Engineering
Blind signal separation and identification of mixtures of images
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
An EM method for spatio-temporal blind source separation using an AR-MOG source model
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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The performance of signal enhancement systems based on adaptive filtering is highly dependent on the quality of the noise reference. In the LMS algorithm, signal leakage into the noise reference leads to signal distortion and poor noise cancellation. The origin of the problem lies in the fact that LMS decorrelates the signal estimate with the noise reference, which, in the case of signal leakage, makes little sense. An algorithm is proposed that decorrelates the signal estimate with a “signal-free” noise estimate, obtained by adding a symmetric filter to the classical structure. The symmetric adaptive decorrelation (SAD) algorithm no longer makes a distinction between signal and noise and is therefore a signal separator rather than a noise canceler. Stability and convergence are of the utmost importance in adaptive algorithms and hence are carefully studied. Apart from limitations on the adaptation constants, stability around the desired solution can only be guaranteed for a subclass of signal mixtures. Furthermore, the decorrelation criterion does not yield a unique solution, and expressions for the “phantom” solutions are derived. Simulations with short FIR filters confirm the predicted behavior