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Continuous-Time Recurrent Neural Networks for Generative and Interactive Musical Performance

  • Oliver Bown
  • Sebastian Lexer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)

Abstract

This paper describes an ongoing exploration into the use of Continuous-Time Recurrent Neural Networks (CTRNNs) as generative and interactive performance tools, and using Genetic Algorithms (GAs) to evolve specific CTRNN behaviours. We propose that even randomly generated CTRNNs can be used in musically interesting ways, and that evolution can be employed to produce networks which exhibit properties that are suitable for use in interactive improvisation by computer musicians. We argue that the development of musical contexts for the CTRNN is best performed by the computer musician user rather than the programmer, and suggest ways in which strategies for the evolution of CTRNN behaviour may be developed further for this context.

Keywords

Hide Node Input State Input Pattern Recurrent Neural Network Input Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Oliver Bown
    • 1
  • Sebastian Lexer
    • 2
  1. 1.Centre for Cognition, Computation and Culture 
  2. 2.Department of Music, Goldsmiths CollegeUniversity of LondonNew CrossUK

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