PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Melody Recognition with Learned Edit Distances
Amaury Habrard, José Iñesta, David Rizo and Marc Sebban
In: S+SSPR 2008, december 2008, Orlando, USA.


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.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4191
Deposited By:Marc Sebban
Deposited On:23 October 2008