This book treats the topic of extending the adaptive filtering
theory in the context of massive multichannel systems by taking
into account a priori knowledge of the underlying system or signal.
The starting point is exploiting the sparseness in acoustic
multichannel system in order to solve the non-uniqueness problem
with an efficient algorithm for adaptive filtering that does not
require any modification of the loudspeaker signals. The book
discusses in detail the derivation of general sparse
representations of acoustic MIMO systems in signal or system
dependent transform domains. Efficient adaptive filtering
algorithms in the transform domains are presented and the relation
between the signal- and the system-based sparse representations is
emphasized. Furthermore, the book presents a novel approach to
spatially preprocess the loudspeaker signals in a full-duplex
communication system. The idea of the preprocessing is to prevent
the echoes from being captured by the microphone array in order to
support the AEC system. The preprocessing stage is given as an
exemplarily application of a novel unified framework for the
synthesis of sound figures. Finally, a multichannel system for the
acoustic echo suppression is presented that can be used as a
postprocessing stage for removing residual echoes. As first of its
kind, it extracts the near-end signal from the microphone signal
with a distortionless constraint and without requiring a
double-talk detector.
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