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Weighted Markov Chain Model for Musical Composer Identification

  • Maximos A. Kaliakatsos-Papakostas
  • Michael G. Epitropakis
  • Michael N. Vrahatis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6625)

Abstract

Several approaches based on the ‘Markov chain model’ have been proposed to tackle the composer identification task. In the paper at hand, we propose to capture phrasing structural information from inter onset and pitch intervals of pairs of consecutive notes in a musical piece, by incorporating this information into a weighted variation of a first order Markov chain model. Additionally, we propose an evolutionary procedure that automatically tunes the introduced weights and exploits the full potential of the proposed model for tackling the composer identification task between two composers. Initial experimental results on string quartets of Haydn, Mozart and Beethoven suggest that the proposed model performs well and can provide insights on the inter onset and pitch intervals on the considered musical collection.

Keywords

Differential Evolution Markov Chain Model Musical Piece Musical Composer Music Information Retrieval 
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 2011

Authors and Affiliations

  • Maximos A. Kaliakatsos-Papakostas
    • 1
  • Michael G. Epitropakis
    • 1
  • Michael N. Vrahatis
    • 1
  1. 1.Computational Intelligence Laboratory (CI Lab), Department of MathematicsUniversity of PatrasPatrasGreece

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