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.
