Symbolic musical genre classification based on repeating patterns

Authors:
Ioannis Karydis;Alexandros Nanopoulos;Yannis Manolopoulos
Affiliations:
Aristotle University, Thessaloniki, Greece;Aristotle University, Thessaloniki, Greece;Aristotle University, Thessaloniki, Greece
Venue:
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
Year:
2006

Citing 3
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Abstract

This paper presents a genre classification algorithm for music data. The proposed methodology relies on note pitch and duration features, derived from the repeating terns and duration histograms of a musical piece, respectively. Note-information histograms have a great capability in capturing a fair amount of information regarding harmonic as well as rhythmic features of different musical genres and pieces, while repeating patterns refer to segments of the piece that are semantically important. Detailed experimental results on intra-classical genres illustrate the significant performance gains due to the proposed features.