Adaptive Identification of Acoustic Multichannel Systems by Karim Helwani

By Karim Helwani

This ebook treats the subject of extending the adaptive filtering concept within the context of big multichannel platforms by way of bearing in mind a priori wisdom of the underlying approach or sign. the start line is exploiting the sparseness in acoustic multichannel method to be able to 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 e-book discusses intimately the derivation of normal sparse representations of acoustic MIMO systems in sign or process established remodel domains. Efficient adaptive filtering algorithms in the rework domain names are awarded and the relation among the sign- and the system-based sparse representations is emphasised. additionally, the booklet offers a singular method of spatially preprocess the loudspeaker indications in a full-duplex verbal exchange procedure. the assumption of the preprocessing is to avoid the echoes from being captured by means of the microphone array with the intention to help the AEC procedure. The preprocessing level is given as an exemplarily application of a unique unified framework for the synthesis of sound figures. Finally, a multichannel approach for the acoustic echo suppression is presented that can be utilized 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 out requiring a double-talk detector.

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Extra resources for Adaptive Identification of Acoustic Multichannel Systems Using Sparse Representations (T-Labs Series in Telecommunication Services)

Example text

This kind of equations can be solved using numerical optimization methods, such as, the Newton algorithm. It should be noted that each eigenfunction corresponds to an eigenfrequency sn that can be obtained by Eq. 19). 40 4 Sparse Representation of Multichannel Acoustic Systems Fig. 2 Sound pressure distribution in the plane z = 0 of a rectangular room for n x = 3 and ny = 5 In the limiting case Z → ∞ it can be easily verified that Bx = 0 and sn x = i nLxxπ where n x ∈ Z. 23) with n y , n z ∈ Z and A x = A y = A z = 1.

3 Source-Domain Estimation The above mentioned separation problem is known in the field of blind source separation (BSS) [11]. BSS techniques aim at jointly diagonalizing the time-lagged covariance matrices of the mixed signals. 3 Source-Domain Estimation 49 x1 farend xP ... ... HS nearend C xH H ... 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].

Buchner H, Benesty J, Gansler T, Kellermann W (2006) Robust extended multidelay filter and double-talk detector for acoustic echo cancellation. IEEE Trans Audio Speech Lang Process 14(5):1633–1644 Chapter 4 Sparse Representation of Multichannel Acoustic Systems In the previous chapter we highlighted the improvement of the convergence rate of Newton based adaptive algorithms by systematically exploiting the sparseness of the system. In the present chapter we will concentrate on a special form of sparsity namely, the diagonal sparsity.

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