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

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    View other formats and editions of Unsupervised Signal Processing by João Marcos Travassos Romano

    Publisher: Taylor & Francis Inc
    Publication Date: 28/09/2010
    ISBN13: 9780849337512, 978-0849337512
    ISBN10: 0849337518
    Also in:
    Computer architecture and logic design Digital signal processing (DSP)

    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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