By Kostas Kokkinakis, Philipos C. Loizou
With human-computer interactions and hands-free communications turning into overwhelmingly vital within the new millennium, fresh examine efforts were more and more concentrating on cutting-edge multi-microphone sign processing recommendations to enhance speech intelligibility in antagonistic environments. One such favourite statistical sign processing approach is blind sign separation (BSS). This publication investigates probably 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 numerous mirrored copies of the unique signs 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 excellent 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 eventualities.
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Extra info for Advances in Modern Blind Signal Separation Algorithms: Theory and Applications
Consequently, the method largely operates on the implicit assumption of a priori knowledge regarding the (unavailable) source densities, in order to yield the PDFs of the true sources when the entropy is maximized. Essentially, this can be viewed as ‘density matching’ between the neuron inputs and expected outputs. Based on Eq. 3), the mutual information I (y) of the network outputs can be expressed in terms of the joint and marginal differential3 entropies as follows: n I (y) = I (y1 , . . 7) If we now combining this with Eq.
Estimate the j th source signal from the mixtures in the frequency-domain: m uj (ω, t) = Wj i (ω) xi (ω, t), j = 1, 2, . . , n. 62) i=1 Step 8. 63) ω=0 Step 9. Return to Step 2 and increment the super-block number. Repeat above until convergence. 2: Summary of the joint diagonalization natural gradient CBSS algorithm 43 44 2. MODERN BLIND SIGNAL SEPARATION ALGORITHMS Step 1. Initialize the separating filters in the frequency-domain. 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 .
51) where in this case W defines the unmixing FIR polynomial matrix11 expressed in the frequencydomain as: ⎛ −1 ⎞ −1 −1 W (j ×i×k) ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ =⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ w11 (k)z−k k=0 −1 k=0 −1 w12 (k)z−k ··· w22 (k)z−k ··· k=0 w21 (k)z−k −1 k=0 .. wn1 (k)z−k k=0 −1 .. wn2 (k)z−k .. ··· k=0 w1m (k)z−k ⎟ ⎟ ⎟ k=0 ⎟ ⎟ −1 ⎟ −k ⎟ w2m (k)z ⎟ ⎟ k=0 ⎟ .. ⎟ ⎟ . 53) k=0 defined for all j = 1, 2, . . , n and i = 1, 2, . . , 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.