By Karim Helwani
This booklet treats the subject of extending the adaptive filtering thought within the context of big multichannel platforms by means of making an allowance for a priori wisdom of the underlying process or sign. the place to begin is exploiting the sparseness in acoustic multichannel approach which will 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 booklet discusses intimately the derivation of common sparse representations of acoustic MIMO systems in sign or approach established remodel domains. Efficient adaptive filtering algorithms in the remodel domain names are provided and the relation among the sign- and the system-based sparse representations is emphasised. in addition, the ebook offers a unique method of spatially preprocess the loudspeaker signs in a full-duplex conversation process. the belief of the preprocessing is to avoid the echoes from being captured through the microphone array with a purpose to help the AEC process. The preprocessing degree is given as an exemplarily application of a unique unified framework for the synthesis of sound figures. Finally, a multichannel procedure for the acoustic echo suppression is presented that can be utilized as a postprocessing level for removal residual echoes. As first of its style, 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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Adaptive Identification of Acoustic Multichannel Systems Using Sparse Representations (T-Labs Series in Telecommunication Services) by Karim Helwani