Musical Style Identification Using Grammatical Inference: The Encoding Problem

  • Pedro P. Cruz-Alcázar
  • Enrique Vidal-Ruiz
  • Juan C. Pérez-Cortés
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

A Musical Style Identification model based on Grammatical Inference (GI) is presented. Under this model, regular grammars are used for modeling Musical Style. Style Classification can be used to implement or improve content based retrieval in multimedia databases, musicology or music education. In this work, several GI Techniques are used to learn, from examples of melodies, a stochastic grammar for each of three different musical styles. Then, each of the learned grammars provides a confidence value of a composition belonging to that grammar, which can be used to classify test melodies. A very important issue in this case is the use of a proper music coding scheme, so different coding schemes are presented and compared, achieving a 3% classification error rate.

Keywords

Multimedia Database Relative Pitch Pitch Interval Musical Style Automatic Composition 
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 2003

Authors and Affiliations

  • Pedro P. Cruz-Alcázar
    • 1
  • Enrique Vidal-Ruiz
    • 2
  • Juan C. Pérez-Cortés
    • 1
  1. 1.Departamento de Informática de Sistemas y ComputadoresUniversidad Politécnica de ValenciaValenciaSpain
  2. 2.Departamento de Sistemas Informáticos y ComputaciónUniversidad Politécnica de ValenciaValenciaSpain

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