Description

Book Synopsis

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 th

    Table of Contents

    Introduction. Statistical Characterization of Signals and Systems. Linear Optimal and Adaptive Filtering. Unsupervised Channel Equalization. Unsupervised Multichannel Equalization. Blind Source Separation. Nonlinear Filtering and Machine Learning. Bio-Inspired Optimization Methods. Appendices.

Unsupervised Signal Processing

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    A Hardback by João Marcos Travassos Romano, Romis Attux, Charles Casimiro Cavalcante

    £161.50

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      Book details

      Publisher CRC Press Inc
      Published 28 September 2010
      ISBN-13 9780849337512
      978-0849337512
      ISBN-10 0849337518

      Description

      Book Synopsis

      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 th

        Table of Contents

        Introduction. Statistical Characterization of Signals and Systems. Linear Optimal and Adaptive Filtering. Unsupervised Channel Equalization. Unsupervised Multichannel Equalization. Blind Source Separation. Nonlinear Filtering and Machine Learning. Bio-Inspired Optimization Methods. Appendices.

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