PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

MUSIC GENRE CLASSIFICATION USING THE TEMPORAL STRUCTURE OF SONGS
Darío García-García, Jerónimo Arenas-Garcia, Emilio Parrado-Hernandez and Fernando Díaz-de-María
In: 2010 IEEE International Workshop on Machine Learning for Signal Processing, 29 Sept - 1 Oct 2010, Kittilä.

Abstract

This paper evaluates the capabilities of model-based distances between time series to identify the musical genre of songs. In contrast with standard approaches, this kind of metrics can take into account the structure of the songs by modeling the dynamics of the parameter sequences. We tackle the problem from a non-supervised and from a supervised perspective, in order to point out the usefulness of dynamic-based distances. Experiments on a real-world dataset containing genres with different degrees of a priori overlapping give insights about the discriminant capabilities of these distances.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:7555
Deposited By:Darío García-García
Deposited On:17 March 2011