Rhythmic Classification of Electronic Dance Music

Leimeister M, Gaertner D, Dittmar C. Rhythmic Classification of Electronic Dance Music. In: AES Conference: 53rd International Conference: Semantic Audio. Audio Engineering Society; 2014. Paper P1-5. Available from: https://aes.org/publications/elibrary-page/?id=17109

@inproceedings{Leimeister2014_17109,
  author = {Leimeister, Matthias and Gaertner, Daniel and Dittmar, Christian},
  title = {{Rhythmic Classification of Electronic Dance Music}},
  booktitle = {AES Conference: 53rd International Conference: Semantic Audio},
  note = {Paper P1-5},
  year = {2014},
  month = jan,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=17109}
}

TY  - CPAPER
TI  - Rhythmic Classification of Electronic Dance Music
AU  - Leimeister, Matthias
AU  - Gaertner, Daniel
AU  - Dittmar, Christian
T2  - AES Conference: 53rd International Conference: Semantic Audio
M1  - Paper P1-5
PY  - 2014
DA  - 2014/01/06
UR  - https://aes.org/publications/elibrary-page/?id=17109
PB  - Audio Engineering Society
LA  - en
AB  - 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%.
ER  -