Blind Source Separation and Independent Component Analysis: A Review (original) (raw)
Related papers
Independent Component Analysis and Blind Signal Separation: Theory, Algorithms and Applications
Learning and Nonlinear Models, 2012
This paper reviews Independent Components Analysis (ICA) and Blind Signal Separation (BSS) problems. An overview on the main statistical principles that guide the search for the independent components is formulated, methods for blind signal separation that require both high-order and second-order statistics are also illustrated. Some of the most successful algorithms for both ICA and BSS are derived. Experimental applications in different signal processing tasks such as passive sonar, nondestructive ultrasound inspection and electrical-load time series are presented.
Blind Source Separation via Independent Component Analysis : Algorithms and Applications
Blind Source Separation (BSS) refers to the process of recovering source signals from a given mixture of unknown source signals were in no prior information about source and mixing methodology is known. Independent Component Analysis (ICA) is widely used BSS technique which allows separation of source components from complex mixture of signals based on certain statistical assumptions. This paper covers up the fundamental concepts of ICA and reviews different algorithms of Independent Component Analysis. In addition, the merits and demerits of each algorithm are outlined. Finally brief description of recent application in ICA is presented.
Semi-blind approaches for source separation and independent component analysis
2006
This paper is a survey of semi-blind source separation approaches. Since Gaussian iid signals are not separable, simplest priors suggest to assume non Gaussian iid signals, or Gaussian non iid signals. Other priors can also been used, for instance discrete or bounded sources, positivity, etc. Although providing a generic framework for semi-blind source separation, Sparse Component Analysis and Bayesian ICA will just sketched in this paper, since two other survey papers develop in depth these approaches.
2004
Our contribution briefly outlines the basics of the well-established technique in data mining, namely the principal component analysis (PCA), and a rapidly emerging novel method, that is, the independent component analysis (ICA). The performance of PCA singular value decomposition-based and stationary linear ICA in blind separation of artificially generated data out of linear mixtures was critically evaluated and compared. All our results outlined the superiority of ICA relative to PCA in faithfully retrieval of the original independent source components.
Independent Component Analysis for Audio and Biosignal Applications
Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, Blind Source Separation (BSS) by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, image processing, telecommunications, medical signal processing and several data mining issues. This book brings the state-of-the-art of some of the most important current research of ICA related to Audio and Biomedical signal processing applications. The book is partly a textbook and partly a monograph. It is a textbook because it gives a detailed introduction to ICA applications. It is simultaneously a monograph because it presents several new results, concepts and further developments, which are brought together and published in the book.
A New Learning Algorithm for Blind Signal Separation
1995
A new on-line learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number of the sources. The Gram-Charlier expansion instead of the Edgeworth expansion is used in evaluating the MI. The natural gradient approach is used to minimize the MI. A novel activation function is proposed for the on-line learning algorithm which has an equivariant property and is easily implemented on a neural network like model. The validity of the new learning algorithm are veried by computer simulations. 3 Lab. for Information Representation, FRP, RIKEN, Wako-shi, Saitama, JAPAN
A Reference Suite Design for Blind Signal Separation
According to the common underlying mathematical model for Independent Component Analysis (ICA), the fulfillment of a BSS for either a linear and scalar type of composition, a convolutive and linear type or a nonlinear type has different conditions. So far, several approaches have been developed in the last decades for stationary and non-stationary data. To identify key research priorities, the different origins of neural network approaches for BSS are briefly reviewed and divided by classes of specific theoretical and application features. A principal guideline for the design of reference data sets for the comparison of all the existing ICA methods by its individual strengths and weaknesses for performing BSS is developed.
Unsupervised learning for blind source separation: an information-theoretic approach
1997 IEEE International Conference on Acoustics, Speech, and Signal Processing
This paper provides a detailed and rigorous analysis of the two commonly used methods for redundancy reduction: Linear Independent Component Analysis (ICA) and Information Maximization (InfoMax). The paper shows analytically that ICA based on the Kullback-Leibler information as a mutual information measure and InfoMax lead to the same solution if the parameterization of the output nonlinear functions in the latter method is sufficiently rich. Furthermore, this work briefly discusses the alternative redundancy measures not based on the Kullback-Leibler information distance and Nonlinear ICA. The practical issues of applying ICA and InfoMax are also discussed.