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Abstract

In a music recognition task, the classification of a new melody is often achieved by looking for the closest piece in a set of already known prototypes. The definition of a relevant similarity measure becomes then a crucial point. So far, the edit distance approach with a-priori fixed operation costs has been one of the most used to accomplish the task. In this paper, the application of a probabilistic learning model to both string and tree edit distances is proposed and is compared to a genetic algorithm cost fitting approach. The results show that both learning models outperform fixed-costs systems, and that the probabilistic approach is able to describe consistently the underlying melodic similarity model.

Keywords

Edit distance learning music similarity genetic algorithms probabilistic models 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Amaury Habrard
    • 1
  • José Manuel Iñesta
    • 2
  • David Rizo
    • 2
  • Marc Sebban
    • 3
  1. 1.Laboratoire d’Informatique FondamentaleUniversité de ProvenceMarseille cedex 13France
  2. 2.Dept. Lenguajes y Sistemas InformáticosUniversidad de AlicanteAlicanteSpain
  3. 3.Laboratoire Hubert CurienUniversité de Saint-EtienneSaint-EtienneFrance

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