Titlebook: Independent Component Analysis and Blind Signal Separation; Fifth International Carlos G. Puntonet,Alberto Prieto Conference proceedings 2

从容

The Minimum Support Criterion for Blind Signal Extraction: A Limiting Case of the Strengthened Youngel criterion for the extraction of the sources whose density has the minimum support measure. By extending the definition of the Renyi’s entropies to include the zero-order case, this criterion can be regarded as part of a more general entropy minimization principle. It is known that Renyi’s entropi

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An Overview of BSS Techniques Based on Order Statistics: Formulation and Implementation Issuess between distributions based on the Cumulative Density Function (cdf). In particular, these gaussianity distances provide new cost functions whose maximization perform the extraction of one independent component at each successive stage of a new proposed deflation ICA procedure. These measures are

Instrumental

Analytical Solution of the Blind Source Separation Problem Using Derivativeselations between mixtures and their derivatives provide a sufficient number of equations for analytically computing the unknown mixing matrix. In addition to its simplicity, the method is able to separate Gaussian sources, since it only requires second order statistics. For two mixtures of two sourc

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秘传

Blind Identification of Complex Under-Determined Mixturesf sources exceeds the dimension of the observation space. The algorithm proposed is able to identify algebraically a complex mixture of complex sources. It improves an algorithm proposed by the authors for mixtures received on a single sensor, also based on characteristic functions. Computer simulat

裹住

Blind Separation of Heavy-Tailed Signals Using Normalized Statistics processes. As the second and higher order moments of the latter are infinite, we propose to use normalized statistics of the observation to achieve the BS of the sources. More precisely, we show that the considered normalized statistics are convergent (i.e., take finite values) and have the appropr

affect

Blind Source Separation of Linear Mixtures with Singular Matricesly sparse. More generally, we consider the problem of identifying the source matrix . ∈ IR. if a linear mixture . = . is known only, where .∈ IR., . ≤ . and the rank of . is less than .. A sufficient condition for solving this problem is that the level of sparsity of . is bigger than .–.(.) in sense

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Closely Arranged Directional Microphone for Source Separationfilter taps while guaranteeing adequate separation performance. We recorded the mixed signals using directional microphones placed close to each other. As a result, we demonstrate that the proposed method successfully separates sources with fewer taps and better separation than conventional methods.