Download PDF by Karim Helwani: Adaptive Identification of Acoustic Multichannel Systems

By Karim Helwani

This e-book treats the subject of extending the adaptive filtering thought within the context of huge multichannel platforms by means of taking into consideration a priori wisdom of the underlying method or sign. the place to begin is exploiting the sparseness in acoustic multichannel method in an effort to remedy the non-uniqueness challenge with a good set of rules for adaptive filtering that doesn't require any amendment of the loudspeaker signals.
The e-book discusses intimately the derivation of basic sparse representations of acoustic MIMO structures in sign or method established remodel domain names. effective adaptive filtering algorithms within the rework domain names are offered and the relation among the sign- and the system-based sparse representations is emphasised. moreover, the publication provides a unique method of spatially preprocess the loudspeaker signs in a full-duplex communique method. the belief of the preprocessing is to avoid the echoes from being captured through the microphone array on the way to help the AEC procedure. The preprocessing level is given as an exemplarily software of a singular unified framework for the synthesis of sound figures. ultimately, a multichannel procedure for the acoustic echo suppression is gifted that may be used as a postprocessing degree for elimination residual echoes. As first of its type, it extracts the near-end sign from the microphone sign with a distortionless constraint and with no requiring a double-talk detector.

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Extra info for Adaptive Identification of Acoustic Multichannel Systems Using Sparse Representations

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To show the tracking performance of the presented algorithm systems a second example is given with filter length L = 64 and very similar scenario but here a system change is simulated after 1 s, by changing the microphone position. Again a stereo system with one microphone is simulated but here each simulated acoustic impulse response is zero except at ten random points. 05. 75 s. All other simulation parameters are the same as in the first experiment. The simulations demonstrate the relation between the sparseness degree of the system and the suitable norm constraint.

H y1 e1 eQ ... C yH ... Cy yQ HS ∗ H Fig. 6 Illustration of echo cancellation in transformed domain of a cost function on a predefined matrix norm. g. the unitarity of the filter matrices. An approach which solves the BSS with this constraint is given in [12]. g. the minimum distortion principle [13] and constraints made on the deconvolution problem. In general the ideal transformation basis is non unitary [14] and Parseval’s theorem cannot be applied unless an optimal separation solution using the unitarity constraint is chosen.

3 General Solution of the Non-Homogeneous Wave Equation The existence of a sound source described by a predefined density function f (r, t) in the domain under consideration V require modifying the Eq. 1 System Sparsity 41 non-homogeneous wave equation. This can be obtained by modifying Eq. 4) to ∂ρ(r, t) + ∇ · (ρ0 v(r)) = −ρ0 f (r, t). ∂t Hence, ∂ρ(r, t) + ρ0 f (r, t) . 25) Substituting in Eq. 12) finally leads with Eq. 26) here we used P to distinguish the solution of the non-homogeneous equation from the homogeneous one.

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