AUTOMATIC CLASSIFICATION OF MIDI TRACKS

Alexandre Bernardo, Thibault Langlois

2008

Abstract

This paper presents a system for classifying MIDI tracks according to six predefined classes: Solo, Melody, Melody+Solo, Drums, Bass and Harmony. No metadata present in the MIDI file is used. The MIDI data (pitch of notes, onset time and note durations) are preprocessed in order to extract a set of features. These data sets are then used with several classifiers (Neural Networks, k-NN).

References

  1. Basili, R., Serafini, A., and Stellato, A. (2004). Classification of musical genre: a machine learning approach. In ISMIR.
  2. Cataltepe, Z., Yaslan, Y., and Sonmez, A. (2007). Music genre classifcation using midi and audio featres. Journal on Advances in Signal Processing, 2007.
  3. Cilbrasi, R., Vitányi, P., and de Wolf, R. (2004). Algoritmic clustering of music based on string compression. Computer Music Journal, 29(4):49-67.
  4. D., R., de León P. J., P., C., P.-S., and Pertusa A., I. J. M. (2006a). A pattern recognition approach for melody track selection in midi files. In Dannenberg R., Lemström K., T. A., editor, Proc. of the 7th Int. Symp. on Music Information Retrieval ISMIR 2006, pages 61- 66, Victoria, Canada. ISBN: 1-55058-349-2.
  5. D., R., de León P.J., P., and Pertusa A., I. J. (2006b). Melodic track identification in midi files. In Proc. of the 19th Int. FLAIRS Conference. AAAI Press. ISBN: 978-1-57735-261-7.
  6. Huang, Y.-P., Guo, G.-L., and Lu, C.-T. (2004). Using back propagation model to design a midi music classification system. In International Computer Symposium, pages 253-258, Taipei, Taiwan.
  7. Li, M. and Sleep, R. (2004a). Improving melody classification by discriminant feature extraction and fusion. In Proc. of the 5th Int. Symp. on Music Information Retrieval ISMIR 2004.
  8. Li, M. and Sleep, R. (2004b). Melody classification using a similarity metric based on kolmogorov complexity. In Sound and Music Computing, Paris, France.
  9. Madsen, S. T. and Widmer, G. (2007). A complexity-based approach to melody track identification in midi files. In International Workshop on Artificial Intelligence and Music (MUSIC-AI 2007), Hyderabad, India.
  10. McKay, C. and Fujinaga, I. (2004). Automatic genre classification using large high-level musical feature sets. In ISMIR.
  11. Ruppin, A. and Yeshurun, H. (2006). Midi music genre classification by invariant features. In ISMIR, pages 397-399.
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Paper Citation


in Harvard Style

Bernardo A. and Langlois T. (2008). AUTOMATIC CLASSIFICATION OF MIDI TRACKS . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-37-1, pages 539-543. DOI: 10.5220/0001708705390543


in Bibtex Style

@conference{iceis08,
author={Alexandre Bernardo and Thibault Langlois},
title={AUTOMATIC CLASSIFICATION OF MIDI TRACKS},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2008},
pages={539-543},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001708705390543},
isbn={978-989-8111-37-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - AUTOMATIC CLASSIFICATION OF MIDI TRACKS
SN - 978-989-8111-37-1
AU - Bernardo A.
AU - Langlois T.
PY - 2008
SP - 539
EP - 543
DO - 10.5220/0001708705390543