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-nearest-neighbour 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%.
Leimeister, Matthias; Gaertner, Daniel; Dittmar, Christian
Affiliation: Fraunhofer Institute for Digital Media Technology, Ilmenau, Germany
AES Conference: 53rd International Conference: Semantic Audio (January 2014)
Paper Number: P1-5
Publication Date: January 27, 2014
Subject: Content-Based Audio Retrieval
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