Music similarity-based approach to generating dance motion sequence

Abstract

In this paper, we propose a novel approach to generating a sequence of dance motions using music similarity as a criterion to find the appropriate motions given a new musical input. Based on the observation that dance motions used in similar musical pieces can be a good reference in choreographing a new dance, we first construct a music-motion database that comprises a number of segment-wise music-motion pairs. When a new musical input is given, it is divided into short segments and for each segment our system suggests the dance motion candidates by finding from the database the music cluster that is most similar to the input. After a user selects the best motion segment, we perform music-dance synchronization by means of cross-correlation between the two music segments using the novelty functions as an input. We evaluate our system’s performance using a user study, and the results show that the dance motion sequence generated by our system achieves significantly higher ratings than the one generated randomly.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Notes

  1. 1.

    In order to show the effect of music-motion synchronization, two video clips before and after synchronization are exemplified for comparison. The video clips can be found at http://plaza4.snu.ac.kr/~park73/download/DMGS.html.

  2. 2.

    The video clips can be found at http://plaza4.snu.ac.kr/~park73/download/DMGS.html.

References

  1. 1.

    Alankus G, Bayazit AA, Bayazit OB (2005) Automated motion synthesis for dancing characters. Comput Animat Virt W 16(3–4):259–271

    Article  Google Scholar 

  2. 2.

    Bartsch MA, Wakefield GH (2001) To catch a chorus: using chroma-based representations for audio thumbnailing. In: 2001 IEEE workshop on the applications of signal processing to audio and acoustics, pp 15–18

  3. 3.

    Foote J (1999) Visualizing music and audio using self-similarity. In: Proc. ACM multimedia, pp 70–80

  4. 4.

    Foote J (2000) Automatic audio segmentation using a measure of audio novelty. In: Proc. IEEE int. conf. multimedia and expo (ICME2000), vol 1, pp 452–455

  5. 5.

    Gray JM (1975) An exploration of musical timbre. PhD thesis, Dept. of Psychology, Stanford University, Stanford, CA, USA (1975)

  6. 6.

    Grunberg D, Ellenberg R, Kim Y, Oh P (2009) Creating an autonomous dancing robot. In: Proceedings of the international conference on hybrid information technology (ICHIT), pp 221–227

  7. 7.

    Ikeuchi K, Shiratori T, Nakazawa A (2006) Dancing-to-music character animation. Comput Graph Forum 25:449–458

    Article  Google Scholar 

  8. 8.

    Kang K-K, Kim D (2007) Synthesis of dancing character motion from beatboxing sounds, smart graphics. In: Lecture notes in computer science, vol 4569. Springer, Berlin, pp 216–219

    Google Scholar 

  9. 9.

    Kanungo T, Netanyahu NS, Wu AY (2002) An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24(7):881–892

    Article  Google Scholar 

  10. 10.

    Kim JW, Fouad H, Sibert JL, Hahn JK (2009) Perceptually motivated automatic dance motion generation for music. Comput Animat Virt W 20:375–384

    Article  Google Scholar 

  11. 11.

    Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recogn 36(2):451–461

    Article  Google Scholar 

  12. 12.

    Loi K-C, Li T-Y (2009) Automatic generation of character animations expressing music features. In: Proceedings of the APSIPA annual summit and conference, pp 216–221

  13. 13.

    Nakahara N, Miyazaki K, Sakamoto H, Fujisawa TX, Nagata N, Nakatsu R (2009) Dance motion control of a humanoid robot based on real-time tempo tracking from musical audio signals. In: Entertainment computing—ICEC 2009, lecture notes in computer science, vol 5709. Springer, Berlin, pp 36–47

    Google Scholar 

  14. 14.

    Nakaoka S, Kajita S, Yokoi K (2010) Intuitive and flexible user interface for creating whole body motions of biped humanoid robots. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 1675–1682

  15. 15.

    Ofli F, Erzin E, Yemez Y, Tekalp AM (2010) Multi-modal analysis of dance performances for music-driven choreography synthesis. In: Proc. IEEE international conference on acoustics speech and signal processing (ICASSP), pp 2466–2469

  16. 16.

    Sandholm A, Pronost N, Thalmann D (2009) MotionLab: a Matlab toolbox for extracting and processing experimental motion capture data for neuromuscular simulations. In: Modelling the physiological human—lecture notes in computer science, vol 5903, pp 110–124

  17. 17.

    Sauer D, Yang Y-H (2009) Music-driven character animation. ACM T Multim Comput 5(4):1–16

    Article  Google Scholar 

  18. 18.

    Shiratori T, Ikeuchi K (2008) Synthesis of dance performance based on analyses of human motion and music. Inf Process Soc JPN 1:80–93

    Google Scholar 

  19. 19.

    Taylor GW, Hinton GE, Roweis ST (2007) Modeling human motion using binary latent variables. In: Advances in neural information processing systems, vol 19, pp 1345–1352

Download references

Acknowledgements

This study was supported by the grant (No. 2011-P3-15) of Advanced Institutes of Convergence Technology (AICT). Also, we greatly acknowledge Dr. Junghoon Kwon for his help on the use of the motion capture system.

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Kyogu Lee or Jaeheung Park.

Additional information

Kyogu Lee and Jaeheung Park are the corresponding authors.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Lee, M., Lee, K. & Park, J. Music similarity-based approach to generating dance motion sequence. Multimed Tools Appl 62, 895–912 (2013). https://doi.org/10.1007/s11042-012-1288-5

Download citation

Keywords

  • Choreography
  • Dance motion generation
  • Music similarity
  • Music-motion database
  • Motion capture
  • Motion synthesis