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.
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.