Melody Recognition with Learned Edit Distances

Authors:
Amaury Habrard;José Manuel Iñesta;David Rizo;Marc Sebban
Affiliations:
Laboratoire d'Informatique Fondamentale, Université de Provence, Marseille cedex 13, France 13453;Dept. Lenguajes y Sistemas Informáticos, Universidad de Alicante, Alicante, Spain E-03080;Dept. Lenguajes y Sistemas Informáticos, Universidad de Alicante, Alicante, Spain E-03080;Laboratoire Hubert Curien, Université de Saint-Etienne, Saint-Etienne, France 42000
Venue:
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2008

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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.