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An Approach to Intelligent Signal Processing

  • Conference paper
Cognitive Behavioural Systems

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7403))

Abstract

This paper describes an approach to intelligent signal processing. First we propose a general signal model which applies to speech, music, biological, and technical signals. We formulate this model mathematically using a unification of hidden Markov models and finite state machines. Then we name tasks for intelligent signal processing systems and derive a hierarchical architecture which is capable of solving them. We show the close relationship of our approach to cognitive dynamic systems. Finally we give a number of application examples.

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Author information

Authors and Affiliations

  1. Lehrstuhl Kommunikationstechnik, Brandenburgische Technische Universität Cottbus, 03046, Cottbus, Germany

    Matthias Wolff

  2. Systemtheorie und Sprachtechnologie, Technische Universität Dresden, 01062, Dresden, Germany

    Rüdiger Hoffmann

Authors
  1. Matthias Wolff
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  2. Rüdiger Hoffmann
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Editor information

Editors and Affiliations

  1. Department of Psychology, and IIASS, Seconda Università degli Studi di Napoli, Italy

    Anna Esposito

  2. Istituto Nazionale di Geofisica e Vulcanologia, sezione di Napoli Osservatorio Vesuviano, Napoli, Italy

    Antonietta M. Esposito

  3. School of Computing Science, University of Glasgow, Glasgow, UK

    Alessandro Vinciarelli

  4. Laboratory of Acoustics and Speech Communication, Technische Universität Dresden, 01062, Dresden, Germany

    Rüdiger Hoffmann

  5. Dept. of Humanities and Social Sciences, Anatolia College/ACT, P.O. Box 21021, 55510, Pylaia, Greece

    Vincent C. Müller

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Wolff, M., Hoffmann, R. (2012). An Approach to Intelligent Signal Processing. In: Esposito, A., Esposito, A.M., Vinciarelli, A., Hoffmann, R., Müller, V.C. (eds) Cognitive Behavioural Systems. Lecture Notes in Computer Science, vol 7403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34584-5_1

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  • DOI: https://doi.org/10.1007/978-3-642-34584-5_1

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