Unsupervised Signal Processing: Channel Equalization and Source
Separation provides a unified, systematic, and synthetic
presentation of the theory of unsupervised signal processing.
Always maintaining the focus on a signal processing-oriented
approach, this book describes how the subject has evolved and
assumed a wider scope that covers several topics, from
well-established blind equalization and source separation methods
to novel approaches based on machine learning and bio-inspired
algorithms.
From the foundations of statistical and adaptive signal
processing, the authors explore and elaborate on emerging tools,
such as machine learning-based solutions and bio-inspired methods.
With a fresh take on this exciting area of study, this book:
- Provides a solid background on the statistical characterization
of signals and systems and on linear filtering theory
- Emphasizes the link between supervised and unsupervised
processing from the perspective of linear prediction and
constrained filtering theory
- Addresses key issues concerning equilibrium solutions and
equivalence relationships in the context of unsupervised
equalization criteria
- Provides a systematic presentation of source separation and
independent component analysis
- Discusses some instigating connections between the filtering
problem and computational intelligence approaches.
Building on more than a decade of the authors work at DSPCom
laboratory, this book applies a fresh conceptual treatment and
mathematical formalism to important existing topics. The result is
perhaps the first unified presentation of unsupervised signal
processing techniques one that addresses areas including digital
filters, adaptive methods, and statistical signal processing. With
its remarkable synthesis of the field, this book provides a new
vision to stimulate progress and contribute to the advent of more
useful, efficient, and friendly intelligent systems.
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