Finding Drum Breaks in Digital Music Recordings

  • Patricio López-SerranoEmail author
  • Christian Dittmar
  • Meinard Müller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11265)


DJs and producers of sample-based electronic dance music (EDM) use breakbeats as an essential building block and rhythmic foundation for their artistic work. The practice of reusing and resequencing sampled drum breaks critically influenced modern musical genres such as hip hop, drum’n’bass, and jungle. While EDM artists have primarily sourced drum breaks from funk, soul, and jazz recordings from the 1960s to 1980s, they can potentially be sampled from music of any genre. In this paper, we introduce and formalize the task of automatically finding suitable drum breaks in music recordings. By adapting an approach previously used for singing voice detection, we establish a first baseline for drum break detection. Besides a quantitative evaluation, we discuss benefits and limitations of our procedure by considering a number of challenging examples.


Music information retrieval Drum break Breakbeat Electronic dance music Audio classification Machine learning 



Patricio López-Serrano is supported by a scholarship from CONACYT-DAAD. Christian Dittmar and Meinard Müller are supported by the German Research Foundation (DFG-MU 2686/10-1). The International Audio Laboratories Erlangen are a joint institution of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Fraunhofer Institute for Integrated Circuits IIS. We would like to thank the organizers of HAMR Hack Day at ISMIR 2016, where the core ideas of the presented work were born.


  1. 1.
    Akkermans, V., Serrá, J.: Shape-based spectral contrast descriptor. In: Proceedings of the Sound and Music Computing Conference (SMC), Porto, Portugal, pp. 143–148 (2009)Google Scholar
  2. 2.
    Van Balen, J.: Automatic recognition of samples in musical audio. Master’s thesis, Universitat Pompeu Fabra, Barcelona, Spain (2011)Google Scholar
  3. 3.
    Van Balen, J., Haro, M., Serrà, J.: Automatic identification of samples in hip hop music. In: International Symposium on Computer Music Modeling and Retrieval (CMMR), London, UK, pp. 544–551 (2012)Google Scholar
  4. 4.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  5. 5.
    Brewster, B., Broughton, F.: Last Night a DJ Saved My Life: The History of the Disc Jockey. Grove Press, New York (2014)Google Scholar
  6. 6.
    Cannam, C., Landone, C., Sandler, M.B.: Sonic visualiser: an open source application for viewing, analysing, and annotating music audio files. In: Proceedings of the ACM International Conference on Multimedia, Firenze, Italy, pp. 1467–1468 (2010)Google Scholar
  7. 7.
    Chen, C., Liaw, A., Breiman, L.: Using random forest to learn imbalanced data. Technical report (2004)Google Scholar
  8. 8.
    Dittmar, C., Lehner, B., Prätzlich, T., Müller, M., Widmer, G.: Cross-version singing voice detection in classical opera recordings. In: Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Málaga, Spain, pp. 618–624 (2015)Google Scholar
  9. 9.
    Driedger, J., Müller, M., Disch, S.: Extending harmonic-percussive separation of audio signals. In: Proceedings of the International Society for Music Information Retrieval (ISMIR), Taipei, Taiwan, pp. 611–616 (2014)Google Scholar
  10. 10.
    Fitzgerald, D.: Harmonic/percussive separation using median filtering. In: Proceedings of the International Conference on Digital Audio Effects (DAFx), Graz, Austria, pp. 246–253 (2010)Google Scholar
  11. 11.
    Hockman, J.A.: An ethnographic and technological study of breakbeats in Hardcore, Jungle, and Drum & Bass. Ph.D. thesis, McGill University, Montreal, Quebec, Canada (2012)Google Scholar
  12. 12.
    Hockman, J.A., Davies, M.E.P., Fujinaga, I.: Computational strategies for breakbeat classification and resequencing in Hardcore, Jungle and Drum & Bass. In: Proceedings of the International Conference on Digital Audio Effects (DAFx), Trondheim, Norway (2015)Google Scholar
  13. 13.
    Jiang, D., Lu, L., Zhang, H.J., Tao, J.H., Cai, L.H.: Music type classification by spectral contrast feature. In: Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Lausanne, Switzerland, vol. 1, pp. 113–116 (2002)Google Scholar
  14. 14.
    Lehner, B., Widmer, G., Sonnleitner, R.: On the reduction of false positives in singing voice detection. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, pp. 7480–7484 (2014)Google Scholar
  15. 15.
    López-Serrano, P., Dittmar, C., Driedger, J., Müller, M.: Towards modeling and decomposing loop-based electronic music. In: Proceedings of the International Conference on Music Information Retrieval (ISMIR), New York, USA, pp. 502–508 (2016)Google Scholar
  16. 16.
    López-Serrano, P., Dittmar, C., Müller, M.: Mid-level audio features based on cascaded harmonic-residual-percussive separation. In: Proceedings of the Audio Engineering Society AES Conference on Semantic Audio, Erlangen, Germany (2017)Google Scholar
  17. 17.
    Müller, M.: Fundamentals of Music Processing. Springer, Cham (2015). Scholar
  18. 18.
    Paulus, J., Müller, M., Klapuri, A.P.: Audio-based music structure analysis. In: Proceedings of the International Society for Music Information Retrieval (ISMIR), Utrecht, The Netherlands, pp. 625–636 (2010)Google Scholar
  19. 19.
    Ratcliffe, R.: A proposed typology of sampled material within electronic dance music. Danc. J. Electron. Danc. Music. Cult. 6(1), 97–122 (2014)CrossRefGoogle Scholar
  20. 20.
    Schloss, J.G.: Making Beats: The Art of Sample-Based Hip-Hop. Music Culture. Wesleyan University Press, Middletown (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Patricio López-Serrano
    • 1
    Email author
  • Christian Dittmar
    • 1
  • Meinard Müller
    • 1
  1. 1.International Audio Laboratories ErlangenErlangenGermany

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