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Learning regular grammars to model musical style: Comparing different coding schemes

  • Pedro P. Cruz-Alcázar
  • Enrique Vidal-Ruiz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1433)

Abstract

An application of Grammatical Inference (GI) in the field of Music Processing is presented, were Regular Grammars are used for modeling musical style. The interest in modeling musical style resides in the use of these models in applications, such as Automatic Composition and Automatic Musical Style Recognition. We have studied three GI Algorithms, which have been previously applied successfully in other fields. In this work, these algorithms have been used to learn a stochastic grammar for each of three different musical styles from examples of melodies. Then, each of the learned grammars was used to stochastically synthesize new melodies (Composition) or to classify test melodies (Style Recognition). Our previous studies in this field showed the need of a proper music coding scheme. Different coding schemes are presented and compared according to results in Composition and Style Recognition. Results from previous studies have been improved.

Keywords

Code Scheme Average Success Rate Musical Style Grammatical Inference Symbol String 
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 1998

Authors and Affiliations

  • Pedro P. Cruz-Alcázar
    • 1
  • Enrique Vidal-Ruiz
    • 2
  1. 1.EPSAUniversidad Politécnica de Valencia, DISCA-AlcoyAlicanteSpain
  2. 2.DSICUniversidad Politécnica de ValenciaValenciaSpain

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