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Pattern Analysis and Applications

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27.09.2016 | Theoretical Advances

A sequential pattern mining approach to design taxonomies for hierarchical music genre recognition

Erschienen in: Pattern Analysis and Applications | Ausgabe 2/2018

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Abstract

In this paper, music genre taxonomies are used to design hierarchical classifiers that perform better than flat classifiers. More precisely, a novel method based on sequential pattern mining techniques is proposed for the extraction of relevant characteristics that enable to propose a vector representation of music genres. From this representation, the agglomerative hierarchical clustering algorithm is used to produce music genre taxonomies. Experiments are realized on the GTZAN dataset for performances evaluation. A second evaluation on GTZAN augmented by Afro genres has been made. The results show that the hierarchical classifiers obtained with the proposed taxonomies reach accuracies of 91.6 % (more than 7 % higher than the performances of the existing hierarchical classifiers).

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Titel: A sequential pattern mining approach to design taxonomies for hierarchical music genre recognition
Publikationsdatum: 27.09.2016
Erschienen in: Pattern Analysis and Applications / Ausgabe 2/2018
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-016-0582-7

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