Kostas Kokkinakis, Philipos C. Loizou's Advances in Modern Blind Signal Separation Algorithms: PDF

By Kostas Kokkinakis, Philipos C. Loizou

ISBN-10: 1608451062

ISBN-13: 9781608451067

With human-computer interactions and hands-free communications changing 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 hostile environments. One such well known statistical sign processing process is blind sign separation (BSS). This ebook investigates some of the most commercially beautiful functions of BSS, that's the simultaneous restoration of indications within a reverberant (naturally echoing) setting, utilizing (or extra) microphones. during this paradigm, every one microphone captures not just the direct contributions from each one resource, but additionally a number of mirrored copies of the unique indications at diverse propagation delays. those recordings are often called the convolutive combos of the unique assets. The objective of this publication within the lecture sequence is to supply perception on contemporary advances in algorithms, that are excellent for blind sign separation of convolutive speech combinations. extra importantly, particular emphasis is given in useful purposes of the constructed BSS algorithms linked to real-life situations.

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Step 2. For every block k = 1, 2, . . , L − 1: Compute the DFT of the ith mixed signal observed at the sensor input: Xi (k) = FFT xi (k − 1)L . . xi (kL − 1) xi (kL) . . 64) k -th block Step 3. 27) is equal to: m Uj (k) = Wj i (k) Xi (k), j = 1, 2, . . , n. 65) i=1 where denotes element-by-element multiplication between the two vectors. Step 4. 66) Step 5. Choose a suitable score function to approximate the PDF of the source, for example the parametric function described in Eq. 67) Step 6. Formulate the (temporary) frequency-domain vector of the nonlinearity ϕi (·), such that: i (k) = FFT 0 · · · 0 ϕi ui (kL) ϕi ui (kL) .

The cochlear implant2 is a small complex electronic device that is implanted under the scalp with electrodes positioned in the cochlea to stimulate the ganglion cells of the auditory nerve (Clark, 2003). Electrical current induces action potentials in the auditory nerve fibers and these are then transmitted directly to the auditory cortex where they are interpreted as sound. Thus, CIs can bypass damaged or missing hair cells within the cochlea that would normally code sound. Cochlear implants have both internal and external components.

67) Step 6. Formulate the (temporary) frequency-domain vector of the nonlinearity ϕi (·), such that: i (k) = FFT 0 · · · 0 ϕi ui (kL) ϕi ui (kL) . . 68) Step 7. 69) where (·)∗ denotes complex conjugation and μ represents a pre-determined step size parameter. Step 8. Output the kth block of the j th recovered source signal estimate in the time-domain: uj (k) = first L terms of IFFT Uj (k) Step 9. Return to Step 2 and increment the block number. Repeat above until convergence. 7. SUMMARY 45 Although the SIRI metric can measure channel identification accuracy reasonably well, it might not always reflect the output speech quality.

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

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