An Independent Component Analysis Evolution Based Method for Nonlinear Speech Processing
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Component (thermodynamics)
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Various blind source separation (BSS) and independent component analysis (ICA) algorithms have been developed. However, comparison study for BSS/ICA algorithms has not been extensively carried out yet. The main objective of this paper is to compare various promising BSS/ICA algorithms in terms of several factors such as robustness to sensor noise, computational complexity, the conditioning of the mixing matrix, the number of sensors, and the number of training patterns. We propose several benchmarks which are useful for the evaluation of the algorithm. This comparison study will be useful for real-world applications, especially EEG/MEG analysis and separation of miked speech signals.
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The Independent Component Analysis (ICA) is a recently developed method for multi-signal processing and Blind Source Separation (BSS). However, its constraint on the sources that the sources are statistically independent of each other greatly limits its applications to BSS since the sources in most applications are not guaranteed to be independent. This paper presents a partially independent component analysis (PICA) method for BSS of dependent sources, where the approximately independent indices of the sources are selected with some feature selection method, and ICA is performed on the selected indices of the observations. A large number of simulations and a real world DNA microarray data experiment show great availability and effectiveness of the method presented here.
Component analysis
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Most of the existed algorithms on BSS(Blind Signal Separation) are based on ICA(Independent Component Analysis).However,ICA has many limits in actual use.In order to solve this(problem,) a new BSS algorithm based on IFA(Independent Factor Analysis) was proposed in this paper.IFA generalizes and unifies ordinary factor,principal component analysis,and ICA.It is proved by the simulation result that IFA can handle the case that the number of mixtures differs from the number of sources and the data include strong noise.The lower is SNR of data,the better is the predominance of IFA.
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Recovering the unobserved source signals from their mixtures is a typical problem in array processing and analysis.Independent component analysis(ICA) is a new method to solve this problem.The most common way in independent component analysis is the separation based on information theory.FastICA algorithm and nature step algorithm are the main way in it.Some groups of signals were separated.The analysis and simulations suggest that the FastICA algorithm is the best way.
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Underdetermined system
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Automated analysis of electrocardiogram (ECG) has got great attention for cardiac diagnosis in the recent years. This paper describes two different ECG analysis algorithms using Independent Component Analysis (ICA) algorithm. ICA refers to set of algorithms for blind source separation (BSS). The underlying principle is to separate N signals from a mix of different source contributions, into signals of independent components. The simulation is proposed to be done in MATLAB.
Component (thermodynamics)
Component analysis
Source Separation
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FastICA
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This paper deals with the study of Independent Component Analysis. Independent Component Analysis is basically a method which is used to implement the concept of Blind Source Separation. Blind Source Separation is a technique which is used to extract set of source signal from set of their mixed source signals. The various techniques which are used for implementing Blind Source Separation totally depends upon the properties and the characteristics of original sources. Also there are many fields nowadays in which Independent Component Analysis is widely used. This paper deals with the theoretical concepts of Independent Component Analysis, its principles and its widely used applications.
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In this paper, a Blind-source separation method, i.e. Independent Component Analysis (ICA) is used for disaggregating the substation load profile into different patterns, i.e. residential and commercial groups. The smart meter data from a down town substation has been used. Principle Component Analysis (PCA) is applied for data reduction. Wavelet analysis is used to extract the trend signal from the original load profile as inputs for the ICA routine. Final results verify the effectiveness of this load profile disaggregation approach.
Load profile
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Component analysis
Source Separation
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