Semi-blind approaches for source separation and independent component analysis (original) (raw)
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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.
Blind Source Separation and Independent Component Analysis: A Review
2005
Blind source separation (BSS) and independent component analysis (ICA) are generally based on a wide class of unsupervised learning algorithms and they found potential applications in many areas from engineering to neuroscience. A recent trend in BSS is to consider problems in the framework of matrix factorization or more general signals decomposition with probabilistic generative and tree structured graphical models and exploit a priori knowledge about true nature and structure of latent (hidden) variables or sources such as spatio-temporal decorrelation, statistical independence, sparseness, smoothness or lowest complexity in the sense e.g., of best predictability. The possible goal of such decomposition can be considered as the estimation of sources not necessary statistically independent and parameters of a mixing system or more generally as finding a new reduced or hierarchical and structured representation for the observed (sensor) data that can be interpreted as physically meaningful coding or blind source estimation. The key issue is to find a such transformation or coding (linear or nonlinear) which has true physical meaning and interpretation. We present a review of BSS and ICA, including various algorithms for static and dynamic models and their applications. The paper mainly consists of three parts:
Sparse Independent Component Analysis with interpolation for Blind Source Separation
2009 2nd International Conference on Computer, Control and Communication, 2009
The separation of a superposition of multiple signals is accomplished by taking into account the structure of the mixing process and by making assumptions about the sources. By assuming that the sources can be represented sparsely in a given basis, recent research has demonstrated that better results can be obtained. In this paper, we will show that increasing the size of mixture of signals by estimating new data points using the technique of interpolation can be used to increase the accuracy of Blind Source Separation (BSS) methods. We propose a four step BSS technique for instantaneous case which increases the accuracy of the sparse BSS methods. These steps include Interpolation, Sparse Decomposition, Independent Component Analysis (ICA) algorithm, and Downsampling. The idea is to use the combination of interpolation and sparsing as preprocessing for ICA. Although the method works for both one dimensional and two dimensional signals, it is best suitable for two dimensional signals like images. Results of the proposed method on one dimensional and two dimensional signals have been presented.
A COMPARATIVE STUDY OF BLIND SOURCE SEPARATION BASED ON PCA AND ICA
IJCRT, 2022
Blind source separation (BSS) consists of the extraction of individual signals from their mixture using no prior knowledge about their nature. Here, we address the blind separation of audio sources by means of Principal component analysis (PCA) and Independent Component Analysis (ICA), which is a popular method for BSS using the assumption that the original sources are mutually independent. PCA and ICA algorithm working for mixed signals is studied and depicted in this paper.
Flexible Bayesian independent component analysis for blind source separation
2001
ABSTRACT Independent Component Analysis (ICA) is an important tool for extracting structure from data. ICA is traditionally performed under a maximum likelihood scheme in a latent variable model and in the absence of noise. Although extensively utilised, maximum likelihood estimation has well known drawbacks such as overfitting and sensitivity to local-maxima.
A semiparametric approach to source separation using independent component analysis
Computational Statistics & Data Analysis, 2013
Data processing and source identification using lower dimensional hidden structure plays an essential role in many fields of applications, including image processing, neural networks, genome studies, signal processing and other areas where large datasets are often encountered. One of the common methods for source separation using lower dimensional structure involves the use of Independent Component Analysis, which is based on a linear representation of the observed data in terms of independent hidden sources. The problem thus involves the estimation of the linear mixing matrix and the densities of the independent hidden sources. However, the solution to the problem depends on the identifiability of the sources. This paper first presents a set of sufficient conditions to establish the identifiability of the sources and the mixing matrix using moment restrictions of the hidden source variables. Under such sufficient conditions a semi-parametric maximum likelihood estimate of the mixing matrix is obtained using a class of mixture distributions. The consistency of our proposed estimate is established under additional regularity conditions. The proposed method is illustrated and compared with existing methods using simulated and real datasets.
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.
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.