Read e-book online Adaptive Identification of Acoustic Multichannel Systems PDF

By Karim Helwani

ISBN-10: 3319089536

ISBN-13: 9783319089539

ISBN-10: 3319089544

ISBN-13: 9783319089546

This ebook treats the subject of extending the adaptive filtering conception within the context of huge multichannel platforms through taking into consideration a priori wisdom of the underlying process or sign. the start line is exploiting the sparseness in acoustic multichannel process so that it will resolve the non-uniqueness challenge with an effective set of rules for adaptive filtering that doesn't require any amendment of the loudspeaker signals.
The publication discusses intimately the derivation of normal sparse representations of acoustic MIMO structures in sign or procedure based remodel domain names. effective adaptive filtering algorithms within the rework domain names are awarded and the relation among the sign- and the system-based sparse representations is emphasised. moreover, the booklet provides a unique method of spatially preprocess the loudspeaker signs in a full-duplex conversation procedure. the assumption of the preprocessing is to avoid the echoes from being captured by means of the microphone array to be able to aid the AEC approach. The preprocessing level is given as an exemplarily program of a singular unified framework for the synthesis of sound figures. ultimately, a multichannel approach for the acoustic echo suppression is gifted that may be used as a postprocessing level for elimination residual echoes. As first of its variety, it extracts the near-end sign from the microphone sign with a distortionless constraint and with out requiring a double-talk detector.

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Additional resources for Adaptive Identification of Acoustic Multichannel Systems Using Sparse Representations

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This is the case if the input signal xm is autocorrelated. , with stereo reproduction systems. In this case the excitation is highly intra- and inter-channel correlated. Strategies to cope with the mentioned ill-conditioning problem aim either at enhancing the conditioning by manipulating the input signals x p , as long as the manipulation can be perceptually tolerated [3, 4], or at regularizing the problem to determine an approximate solution that is stable under small changes in the initial data.

5 Ill-Conditioning in Multichannel Adaptive Filtering and Sparseness Constraint An advantage of the regularization due to a 2 -constraint is that the 2 regularization aims at adding the same value to all eigenvalues of an ill-conditioned system. This has the positive effect that all eigenvalues are prevented from becoming zero, hence, they can be inverted and an inversion of the resulting regularized system is ensured. But the resulting system could still have eigenvalues with high multiplicity.

Studying this case offers insights into the properties of the sparseness based regularization in the context of multichannel adaptive filtering and as we will see this choice of the norm parameter leads to an efficient implementation strategy since the regularization matrix B becomes diagonal as discussed above. 9) hereby, sgn(·) = |·|· stands for the sign function. 8f) are then given as 28 3 Spatio-Temporal Regularized Recursive Least Squares Algorithm Fig. 2 Schematic illustration of the influence of the 1 -norm regularization on the Hessian matrix Gdiag ∂2 h p ∂ hˆ 2 p p = p(p − 1)|hˆ m,l |(p−2) .

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Adaptive Identification of Acoustic Multichannel Systems Using Sparse Representations by Karim Helwani

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