Pattern-based Classification of Rhythms

Johannes Fliege, Frank Seifert, André Richter


We present a pattern-based approach for the rhythm classification task that combines an Auto-correlation function (ACF) and Discrete Fourier transform (DFT). Rhythm hypotheses are first extracted from symbolic input data, e.g. MIDI, by the ACF. These hypotheses are analysed by the use of DFT to remove duplicates before the classification process. The classification of rhythms is performed using ACF in combination with rhythm patterns contained in a knowledge base. We evaluate this method using pre-labelled input data and discuss our results. We show that a knowledge-based approach is reasonable to address the problem of rhythm classification for symbolic data.


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Paper Citation

in Harvard Style

Fliege J., Seifert F. and Richter A. (2014). Pattern-based Classification of Rhythms . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 747-752. DOI: 10.5220/0004919507470752

in Bibtex Style

author={Johannes Fliege and Frank Seifert and André Richter},
title={Pattern-based Classification of Rhythms},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

in EndNote Style

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Pattern-based Classification of Rhythms
SN - 978-989-758-018-5
AU - Fliege J.
AU - Seifert F.
AU - Richter A.
PY - 2014
SP - 747
EP - 752
DO - 10.5220/0004919507470752