Sleep Musicalization: Automatic Music Composition from Sleep Measurements

  • Aurora Tulilaulu
  • Joonas Paalasmaa
  • Mikko Waris
  • Hannu Toivonen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)


We introduce data musicalization as a novel approach to aid analysis and understanding of sleep measurement data. Data musicalization is the process of automatically composing novel music, with given data used to guide the process. We present Sleep Musicalization, a methodology that reads a signal from state-of-the-art mattress sensor, uses highly non-trivial data analysis methods to measure sleep from the signal, and then composes music from the measurements. As a result, Sleep Musicalization produces music that reflects the user’s sleep during a night and complements visualizations of sleep measurements. The ultimate goal is to help users improve their sleep and well-being. For practical use and later evaluation of the methodology, we have built a public web service at for users of the sleep sensors.


Sleep Stage Deep Sleep Sleep Measurement Light Sleep Composition Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Paalasmaa, J., Leppakorpi, L., Partinen, M.: Quantifying respiratory variation with force sensor measurements. In: 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011, pp. 3812–3815 (2011)Google Scholar
  2. 2.
    Paalasmaa, J., Waris, M., Toivonen, H., Leppakorpi, L., Partinen, M.: Unobtrusive Online Monitoring of Sleep at Home. In: 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012, pp. 3784–3788 (2012)Google Scholar
  3. 3.
    Iber, C., Ancoli-Israel, S., Chesson, A., Quan, S.F.: The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. American Academy of Sleep Medicine (2007)Google Scholar
  4. 4.
    Morgenthaler, T., Alessi, C., Friedman, L., Owens, J., Kapur, V., Boehlecke, B., Brown, T., Chesson Jr., A., Coleman, J., Lee-Chiong, T., Pancer, J., Swick, T.: Practice parameters for the use of actigraphy in the assessment of sleep and sleep disorders: An update for 2007. Sleep 30(4), 519–529 (2007)Google Scholar
  5. 5.
    Shambroom, J.R., Fabregas, S.E., Johnstone, J.: Validation of an automated wireless system to monitor sleep in healthy adults. Journal of Sleep Research 21(2), 221–230 (2012)CrossRefGoogle Scholar
  6. 6.
    Kramer, G., Walker, B.N.: Sound science: Marking ten international conferences on auditory display. ACM Transactions on Applied Perception (TAP) 2(4), 383–388 (2005)CrossRefGoogle Scholar
  7. 7.
    Henry, T.K.: Invention locates hurt brain cells. New York Times 21 (March 2, 1943)Google Scholar
  8. 8.
    Knapp, R.B., Lusted, H.S.: A bioelectric controller for computer music applications. Computer Music Journal 14(1), 42–47 (1990)CrossRefGoogle Scholar
  9. 9.
    Mann, S., Fung, J., Garten, A.: DECONcert: bathing in the light, sound, and waters of the musical brainbaths. In: ICMC 2007: International Computer Music Conference (2007)Google Scholar
  10. 10.
    Le Groux, S., Manzolli, J., Verschure, P.F.M.J.: Disembodied and collaborative musical interaction in the multimodal brain orchestra. In: NIME 2010: Proceedings of the International Conference on New Interfaces for Musical Expression, pp. 309–314 (2010)Google Scholar
  11. 11.
    Kirchmeyer, H.: On the historical constitution of a rationalistic music. Die Reihe 8, 11–24 (1968)Google Scholar
  12. 12.
    Roads, C.: The computer music tutorial. The MIT Press (1996)Google Scholar
  13. 13.
    Muscutt, K.: Composing with algorithms: An interview with David Cope. Computer Music Journal 31(3), 10–22 (2007)CrossRefGoogle Scholar
  14. 14.
    King, R.D., Angus, C.G.: PM – protein music. Bioinformatics 12(3), 251–252 (1996)CrossRefGoogle Scholar
  15. 15.
    Pinheiro, E., Postolache, O., Girao, P.: Theory and developments in an unobtrusive cardiovascular system representation: Ballistocardiography. Open Biomedical Engineering Journal 4, 201–216 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Aurora Tulilaulu
    • 1
  • Joonas Paalasmaa
    • 1
    • 2
  • Mikko Waris
    • 2
  • Hannu Toivonen
    • 1
  1. 1.Department of Computer Science and HIITUniversity of HelsinkiFinland
  2. LtdEspooFinland

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