Speech enhancement is a classical problem in signal processing,
yet still largely unsolved. Two of the conventional approaches for
solving this problem are linear filtering, like the classical
Wiener filter, and subspace methods. These approaches have
traditionally been treated as different classes of methods and have
been introduced in somewhat different contexts. Linear filtering
methods originate in stochastic processes, while subspace methods
have largely been based on developments in numerical linear algebra
and matrix approximation theory.
This book bridges the gap between these two classes of methods
by showing how the ideas behind subspace methods can be
incorporated into traditional linear filtering. In the context of
subspace methods, the enhancement problem can then be seen as a
classical linear filter design problem. This means that various
solutions can more easily be compared and their performance bounded
and assessed in terms of noise reduction and speech distortion. The
book shows how various filter designs can be obtained in this
framework, including the maximum SNR, Wiener, LCMV, and MVDR
filters, and how these can be applied in various contexts, like in
single-channel and multichannel speech enhancement, and in both the
time and frequency domains.
First short book treating subspace approaches in a unified way for
time and frequency domains, single-channel, multichannel, as well
as binaural, speech enhancement. Bridges the gap between optimal
filtering methods and subspace approaches.Includes original
presentation of subspace methods from different perspectives.
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