By Kostas Kokkinakis, Philipos C. Loizou
With human-computer interactions and hands-free communications turning into overwhelmingly very important within the new millennium, contemporary learn efforts were more and more targeting cutting-edge multi-microphone sign processing strategies to enhance speech intelligibility in adversarial environments. One such popular statistical sign processing procedure is blind sign separation (BSS). This publication investigates probably the most commercially appealing 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 assorted propagation delays. those recordings are often called the convolutive combinations of the unique resources. The aim of this book within the lecture sequence is to supply perception on contemporary advances in algorithms, that are preferrred for blind sign separation of convolutive speech combinations. extra importantly, particular emphasis is given in functional functions of the constructed BSS algorithms linked to real-life eventualities.
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Extra resources for Advances in Modern Blind Signal Separation Algorithms: Theory and Applications
In this case, the steepest direction can be also approximated by the so-called natural gradient instead, which can be simply viewed as an optimal rescaling of the standard (stochastic) entropy gradient. Learning using the natural gradient proves to be a much more efficient option since (1) the algorithm no longer requires the computationally expensive estimation of the transpose of the inverse term W −T in the update equation and (2) the steady-state performance, as well as the convergence rate are greatly improved over the standard (stochastic) gradient optimization scheme.
Such approaches generally improve the signal-to-noise ratio (SNR) performance, especially when the speech and noise sources are spatially separated. 1 HEARING LOSS In general, depending on which part of the ear is affected, hearing loss can be classified into conductive and sensorineural (Moore, 2007). Conductive hearing loss is caused by problems in the outer and middle ear interfering with the transmission of sound to the inner ear. This type of hearing loss is the most common cause of hearing impairment especially in children.
11), Eq. 13) Thus, from Eq. 4) and Eq. 8), it follows that the maximization of the output joint entropy H (u) or alternatively the minimization of mutual information I (u) can be achieved by building a simple cost (risk) log-likelihood function, such that: n G (u, W ) = E log pui (ui ) + log | det(W ) | i=1 4 See Eq. 6) in Chapter 1. 14) 28 2. 15) To proportionally update the separating matrix W with respect to its entropy gradient in Eq. 16) where μ denotes a suitable step size (or learning rate) and ϕ(u) = [ϕ1 (u1 ), .
Advances in Modern Blind Signal Separation Algorithms: Theory and Applications by Kostas Kokkinakis, Philipos C. Loizou