Emotion Recognition of Violin Music based on Strings Music Theory for Mascot Robot System

Z.-T. Liu, Z. Mu, L.-F. Chen, C. Fatichah, F. Yan, J.-J. Lu, K. Ohnishi, M. L. Tangel, M. Yamaguchi, P. Q. Le, T.-Y. Li, Y. Adachi, Y.-K. Tang, Y. Yamazaki, F.-Y. Dong, K. Hirota

2012

Abstract

Emotion recognition of violin music is proposed based on strings music theory, where the emotional state of violin music is expressed by Affinity-Pleasure-Arousal emotion space. Besides the music features from audio processing, three features (i.e., left-hand feature, right-hand feature, and dynamics) with regard to both composition and performance of violin music, are extracted to improve the emotion recognition of violin music. To demonstrate the validity of this proposal, a dataset composing of 120 pieces of author-performed violin music with six primary emotion categories is established, by which the experimental results of emotion recognition using Support Vector Regression report overall recognition accuracy of 86.67%. The proposal could be an integral part for analyzing the communication atmosphere with background music, or be used by a music recommendation system for various occasions.

References

  1. Ayadi M. E., Kamel M. S., and Karray F., 2011. Survey on speech emotion recognition: features, classification schemes, and databases. Pattern Recognition. 44 (3): 572-587.
  2. Barbour J. M., 2004. Tuning and Temperament: A Historical Survey, Courier Dover Publications. New York, Dover edition.
  3. Boersma P. and Weenink D., 2008. Praat: doing phonetics by computer. http://www.praat.org/.
  4. Brown C., 1999. Classical and Romantic Performing Practice 1750-1900, Oxford University Press.
  5. Cabrera D., 1999. PSYSOUND: A computer program for psychoacoustical analysis. Proceedings of the Australian Acoustical Society Conference.
  6. Grimm, M., Kroschel, K., and Narayanan S., 2007. Support vector regression for automatic recognition of spontaneous emotions in speech. IEEE Int. Conf. on Acoustics, Speech and Signal Processing.
  7. Han B.-J., Rho S., Dannenberg R. B., and Hwang E., 2009. SMERS: music emotion recognition using Support Vector Regression. 10th International Conference on Music Information Retrieval.
  8. Hargreaves D. J., 1999. The functions of music in everyday life: redefining the social in music psychology. Psychology of Music. 27 (1): 71-83.
  9. Hirota K. and Dong F.-Y., 2008. Development of Mascot Robot System in NEDO project. In Proc. 4th IEEE Int. Conf. Intelligent Systems.
  10. Juslin P. N., 2000. Cue utilization in communication of emotion in music performance: relating performance to perception. Journal of Experimental Psychology: Human Perception of Performance. 26 (6): 1797-1813.
  11. Kim Y. E., Schmidt E. M., Migneco R., Morton B. G., Richardson P., Scott J., Speck J. A., and Turnbull D., 2010. Music emotion recognition: a state of the art review. 11th International Society for Music Information Retrieval Conference.
  12. Lartillot O. and Toiviainen P., 2007. A Matlab toolbox for musical feature extraction from audio. 10th Int. Conference on Digital Audio Effects.
  13. Lee C.-C., Mower E., Busso C., Lee S., and Narayanan S., 2011. Emotion recognition using a hierarchical binary decision tree approach. Speech Communication. 53 (9- 10): 1162-1171.
  14. Liu Z.-T., Wu M., Li D.-Y., Dong F.-Y., Yamakaki Y., and Hirota K., 2011. Emotional states based 3-D Fuzzy Atmosfield for casual communication between humans and robots. In Proc. IEEE Int. Conf. Fuzzy Systems.
  15. Lu L., Liu D., and Zhang H.-J., 2006. Automatic mood detection and tracking of music audio signals. IEEE Transactions on Audio, Speech, and Language Processing. 14 (1): 5-18.
  16. Nakamura T., 1987. The communication of dynamics between musicians and listeners through musical performance. Perception & Psychophysics. 41 (6): 525-533.
  17. Oatley K., Johnson-Laird P.N., 1987. Towards a cognitive theory of emotions. Cognition & Emotion. 1: 29-50.
  18. Sethares W. A., 1993. Local consonance and the relationship between timbre and scale. Journal of the Acoustical Society of America. 94 (3): 1218-1228.
  19. Starks E., 2012. Popular instruments used in classical music. http://www.ehow.com/about_5377304_popular -instruments-used-classical-music.html.
  20. Smola A. J. and Schölkopf B., 2004. A tutorial on Support Vector Regression. Statistics and Computing. 14 (3): 199-222.
  21. Yamazaki Y., Hatakeyama Y., Dong F. Y., and Hirota K., 2008. Fuzzy inference based mentality expression for eye robot in Affinity Pleasure-Arousal space. Jounal of Advanced Computational Intelligence and Intelligent Informatics. 12 (3): 304-313.
  22. Yamazaki Y., Vu H. A., Le P. Q., Liu Z.-T., Fatichah C., Dai M., Oikawa H., Masano D., Thet O., Tang Y.-K., Nagashima N., Tangel M. L., Dong F.-Y., and Hirota K., 2010. Gesture recognition using combination of acceleration sensor and images for casual communication between robots and humans. IEEE Congress on Evolutionary Computation.
  23. Yang Y.-H., Lin Y.-C., Su Y.-F., and Chen H. H., 2008. A regression approach to music emotion recognition. IEEE Transactions on Audio, Speech, and Language Processing. 16 (2): 448-457.
  24. Zweifel P. F., 2005. The mathematical physics of music. Journal of Statistical Physics. 121 (5-6): 1097-1104.
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Paper Citation


in Harvard Style

Liu Z., Mu Z., Chen L., Yamaguchi M., Yan F., Tang Y., Ohnishi K., Tangel M., Lu J., Li T., Le P., Fatichah C., Adachi Y., Yamazaki Y., Dong F. and Hirota K. (2012). Emotion Recognition of Violin Music based on Strings Music Theory for Mascot Robot System . In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8565-21-1, pages 5-14. DOI: 10.5220/0003991500050014


in Bibtex Style

@conference{icinco12,
author={Z.-T. Liu and Z. Mu and L.-F. Chen and M. Yamaguchi and F. Yan and Y.-K. Tang and K. Ohnishi and M. L. Tangel and J.-J. Lu and T.-Y. Li and P. Q. Le and C. Fatichah and Y. Adachi and Y. Yamazaki and F.-Y. Dong and K. Hirota},
title={Emotion Recognition of Violin Music based on Strings Music Theory for Mascot Robot System},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2012},
pages={5-14},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003991500050014},
isbn={978-989-8565-21-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Emotion Recognition of Violin Music based on Strings Music Theory for Mascot Robot System
SN - 978-989-8565-21-1
AU - Liu Z.
AU - Mu Z.
AU - Chen L.
AU - Yamaguchi M.
AU - Yan F.
AU - Tang Y.
AU - Ohnishi K.
AU - Tangel M.
AU - Lu J.
AU - Li T.
AU - Le P.
AU - Fatichah C.
AU - Adachi Y.
AU - Yamazaki Y.
AU - Dong F.
AU - Hirota K.
PY - 2012
SP - 5
EP - 14
DO - 10.5220/0003991500050014