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Rhythmic classification of electronic dance music

 
: Leimeister, Matthias; Gärtner, Daniel; Dittmar, Christian

Dittmar, C. ; Audio Engineering Society -AES-:
53rd International Conference on Semantic Audio 2014 : London, United Kingdom 26 – 29 January 2014
Red Hook, NY: Curran, 2014
ISBN: 978-1-63266-284-2
ISBN: 1-63266-284-1
pp.71-79
International Conference on Semantic Audio <53, 2014, London>
English
Conference Paper
Fraunhofer IDMT ()
music classification; genre classification

Abstract
Electronic dance music can be characterised to a large extent by its rhythmic properties. Besides the tempo, the basic rhythmic patterns play a major role. In this work we present a system that uses these features to classify electronic music tracks into subgenres. From each song, a drum pattern of 4 bars length is extracted incorporating source separation techniques, consisting of bass drum and snare drum events quantized to 16th notes. After determining the downbeat, the measure-aligned pattern serves as a feature in a k-nearestneighbour classification task. The system is evaluated on a dataset containing excerpts from 400 songs from eight electronic subgenres. As a baseline, the classification using solely the tempo as a feature is performed, achieving a classification accuray of 66%. The additional feature of rhythm pattern increases the performance to 71%.

: http://publica.fraunhofer.de/documents/N-301556.html