By Kostas Kokkinakis, Philipos C. Loizou

ISBN-10: 1608451062

ISBN-13: 9781608451067

With human-computer interactions and hands-free communications turning into overwhelmingly vital within the new millennium, fresh examine efforts were more and more targeting cutting-edge multi-microphone sign processing ideas to enhance speech intelligibility in antagonistic environments. One such well known statistical sign processing process is blind sign separation (BSS). This publication investigates some of the most commercially beautiful functions of BSS, that's the simultaneous restoration of indications within a reverberant (naturally echoing) surroundings, utilizing (or extra) microphones. during this paradigm, every one microphone captures not just the direct contributions from every one resource, but in addition a number of mirrored copies of the unique indications at diverse propagation delays. those recordings are known as the convolutive combinations of the unique resources. The aim of this publication within the lecture sequence is to supply perception on fresh advances in algorithms, that are ideal for blind sign separation of convolutive speech combinations. extra importantly, particular emphasis is given in sensible purposes of the constructed BSS algorithms linked to real-life eventualities.

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Thus, it is further possible to show that the relative gradient rule is in fact equivalent to the NGA update of Eq. 19). 24) which clearly indicates that the natural and relative gradient algorithms are in fact equivalent. Another interesting property of the aforementioned adaptive algorithms is that they belong to the class of the so-called equivariant algorithms. In short, this implies that their performance is completely characterized by the global system G, while it is altogether independent of the individual values of the mixing matrix A.

19). 24) which clearly indicates that the natural and relative gradient algorithms are in fact equivalent. Another interesting property of the aforementioned adaptive algorithms is that they belong to the class of the so-called equivariant algorithms. In short, this implies that their performance is completely characterized by the global system G, while it is altogether independent of the individual values of the mixing matrix A. 25) At every iteration , the expression in Eq. 25) depends only on the global system matrix G , the source signal s, and the step size μ, whilst it is entirely independent on the nature and equivalently the 30 2.

Their entropy (or information) maximization algorithm, known as INFOMAX, quickly catalyzed a surge of interest in using information theory for BSS. Initially, the main focus was to just maximize the information passed through a single neuron2 . Yet, the authors quickly discovered that maximizing the information flow in a neural network can be interpreted as a way of approaching ICA, an observation which had been noted earlier by Nadal and Parga (1994). The crux principle here is the maximization of the mutual information I (y, u) transferred between a random input vector u and an invertible linear transformation of it defined as y = g(u) = g(W x), where g(·) is a nonlinear function, which approximates the (hypothesized) cumulative density function (CDF) of the unknown sources.

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Advances in Modern Blind Signal Separation Algorithms: Theory and Applications by Kostas Kokkinakis, Philipos C. Loizou


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