New equations and iterative algorithm for blind separation of sources
Signal Processing
The Minimum Entropy and Cumulants Based Contrast Functions for Blind Source Extraction
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Image denoising using self-organizing map-based nonlinear independent component analysis
Neural Networks - New developments in self-organizing maps
Characteristic-function-based independent component analysis
Signal Processing - Special section: Security of data hiding technologies
Blind separation of jointly stationary correlated sources
Signal Processing - Special issue on independent components analysis and beyond
Adaptive blind separation with an unknown number of sources
Neural Computation
IEEE Transactions on Knowledge and Data Engineering
An Adaptive Method for Subband Decomposition ICA
Neural Computation
Blind sparse source separation using cluster particle swarm optimization technique
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
A blind source separation-based method for multiple images encryption
Image and Vision Computing
Performance analysis of single channel blind source separation
ISPRA'08 Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation
Single channel audio source separation
WSEAS Transactions on Signal Processing
Blind source separation with dynamic source number using adaptive neural algorithm
Expert Systems with Applications: An International Journal
A Cascade System for Solving Permutation and Gain Problems in Frequency-Domain BSS
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Application of Independent Component Analysis to Edge Detection and Watermarking
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Reconstructing Data Perturbed by Random Projections When the Mixing Matrix Is Known
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Blind source separation based on cumulants with time and frequency non-properties
IEEE Transactions on Audio, Speech, and Language Processing
A quadratic programming approach to blind equalization and signal separation
IEEE Transactions on Signal Processing
Sequential extraction algorithm for BSS without error accumulation
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Blind source separation-based encryption of images and speeches
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
A fast decryption algorithm for BSS-Based image encryption
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Differential fast fixed-point BSS for underdetermined linear instantaneous mixtures
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Fourth-Order cumulants and neural network approach for robust blind channel equalization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
On the connection between the human visual system and independent component analysis
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Connections between ICA and sparse coding revisited
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Simulated annealing Based-GA using injective contrast functions for BSS
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
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This paper identifies and studies two major issues in the blind source separation problem: separability and separation principles. We show that separability is an intrinsic property of the measured signals and can be described by the concept of m-row decomposability introduced in this paper; we also show that separation principles can be developed by using the structure characterization theory of random variables. In particular, we show that these principles can be derived concisely and intuitively by applying the Darmois-Skitovich theorem, which is well known in statistical inference theory and psychology. Some new insights are gained for designing blind source separation filters