By Kostas Kokkinakis, Philipos C. Loizou
With human-computer interactions and hands-free communications turning into overwhelmingly very important within the new millennium, fresh study efforts were more and more concentrating on cutting-edge multi-microphone sign processing ideas to enhance speech intelligibility in opposed environments. One such well-known statistical sign processing approach is blind sign separation (BSS). This publication investigates probably the most commercially beautiful purposes of BSS, that is the simultaneous restoration of indications inside of 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 additionally numerous mirrored copies of the unique signs at varied propagation delays. those recordings are known as the convolutive combos of the unique resources. The target of this ebook within the lecture sequence is to supply perception on contemporary advances in algorithms, that are ultimate for blind sign separation of convolutive speech combinations. extra importantly, particular emphasis is given in functional purposes of the constructed BSS algorithms linked to real-life situations.
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Additional info for Advances in Modern Blind Signal Separation Algorithms: Theory and Applications
M, which in essence, describes a moving-average (MA) process. In addition, the operators FFT[ · ] and IFFT[ · ] represent the Fast Fourier and the Inverse Fast Fourier Transform, respectively, defined here as element-wise operations on the filter, whereas the term FFT [ϕ(u)] denotes the frequency-domain representation of the nonlinear activation (score) function ϕ(u), such that: 11The term FIR polynomial matrix is used here to denote either the z-domain or the short-time discrete Fourier domain represen- tation of any scalar matrix in the frequency-domain.
2π (T − 1)/T , we can obtain an estimate of the cross-spectral density matrix of the recovered sources Ru (ω, k) at different super-blocks k = 0, 1, . . 43) m=0 where T denotes the frame size of the DFT, M = T (1 − β)/(KβT ) is the number of overlapping consecutive intervals used to estimate each cross-power matrix and 0 < β ≤ 1 is the overlapping factor. 44) ω=0 k=0 where · 2F is the squared Frobenius norm (sum of squares of all elements) of the error matrix V(ω, k) = off [Ru (ω, k)], where operator off [ · ] zeros the diagonal elements of matrix Ru (ω, k).
This type of hearing loss is caused by the gradual loss and subsequent death of the delicate hair cells within the inner ear. Other common causes for the destruction of hair cells are congenital defects, excessive exposure to noise and certain viral infections or side-effects from intravenous medication. The damage to the hair cells associated with sensorineural hearing loss produces the following changes in the perception of sound (1) increased hearing threshold or, in other words, loss of sensitivity to weak sounds, (2) reduced frequency selectivity which contributes mainly to difficulty in discriminating the spectral shape of speech and (3) reduced dynamic range.
Advances in Modern Blind Signal Separation Algorithms: Theory and Applications by Kostas Kokkinakis, Philipos C. Loizou