An Extended Interactive Evolutionary Computation Using Heart Rate Variability as Fitness Value for Composing Music Chord Progression
Makoto Fukumoto*, Shuta Nakashima**, Shintaro Ogawa**,
and Jun-ichi Imai***
*Faculty of Computer Science and Engineering, Fukuoka Institute of Technology
**Graduate School of Engineering, Fukuoka Institute of Technology, 3-30-1 Wajirohigashi, Higashi-ku, Fukuoka-shi, Fukuoka 811-0295, Japan
***Faculty of Information and Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino-shi, Chiba 275-0016, Japan
Interactive Evolutionary Computation (IEC) is known as an efficient method to create media content suited to the individual user. To reduce user’s fatigue, which remains as a serious problem in IEC, extended IEC that uses physiological information as a fitness value have been proposed. As a new extended IEC, this study proposed extended IEC using Heart Rate Variability (HRV), which reflects autonomic nervous activity. A High Frequency (HF) component of HRV was used as the fitness value. Two listening experiments were conducted to determine the efficacy of the proposed method. In experiment 1, with a concrete system of the proposed method creating music chord progression, a change in the fitness value was observed. In experiment 2, representative created music chord progressions were evaluated subjectively. The change in the fitness value of the HF component showed no gradual increase. Subjective evaluation results showed that the lowest fitness value was observed in the 1st generation, and the fitness value in the 10th generation significantly increased from the 1st generation (P < 0.05). The result of the subjective evaluation showed the efficacy of the proposed method.
and Jun-ichi Imai, “An Extended Interactive Evolutionary Computation Using Heart Rate Variability as Fitness Value for Composing Music Chord Progression,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.9, pp. 1329-1336, 2011.
-  H. Takagi, “Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation,” Proc. the IEEE, Vol.89, No.9, pp. 1275-1296, 2001.
-  R. Dawkins, “The Blind Watchmaker,” 1986.
-  M. Herdy, “Evolutionary optimization based on subjective selection evolving blends of coffee,” in Proc. 5th European Congress on Intelligent Techniques and Soft Computing, Aachen, pp. 640-644, 1997.
-  H. Nishino, K. Takekata, M. Sakamoto, B. A. Salzman, T. Kagawa, and K. Utsumiya, “An IEC-Based Haptic Rendering Optimizer,” in Proc. the IEEE WSTST5, Springer, pp. 653-662, 2005.
-  M. Fukumoto, M. Inoue, and J. Imai, “User’s Favorite Scent Design Using Paired Comparison-based Interactive Differential Evolution,” in Proc. 2010 IEEE Congress on Evolutionary Computation, Barcelona, June 2010, pp. 4519-4524, 2010.
-  R. W. Picard, E. Vyzas, and J. Healey, “Toward machine emotional intelligence: analysis of affective physiological state,” IEEE Trans. on Pattern Analysis andMachine Intelligence, Vol.23, No.10, pp. 1175-1191, 2001.
-  H. Takagi, S.Wang, and S. Nakano, “Proposal for a Framework for Optimizing Artificial Environments Based on Physiological Feedback,” J. of Physiological Anthropology and Applied Human Science, Vol.24, No.1, pp. 77-80, 2005.
-  D. Pallez, P. Collard, T. Baccino, and L. Dumercy, “Eye-tracking evolutionary algorithm to minimize user fatigue in IEC applied to interactive one-max problem,” in Proc. 2007 GECCO conference companion on Genetic and evolutionary computation, pp. 2883-2886, 2007.
-  H. Takagi, “New IEC Research and Frameworks,” in Aspects of Soft Computing, Intelligent Robotics and Control edited by J. Fodor and J. Kacprzyk, Springer-Verlag, Berlin Heidelberg, pp. 65-78, 2009.
-  M. Fukumoto and J. Imai, “Evolutionary computation system for musical composition using listener’s heartbeat information,” IEEJ Trans. on Electrical and Electronic Engineering, Vol.3, No.6, pp. 629-631, 2008.
-  M. Fukumoto, T. Hazama, and J. Imai, “Evolutionary computation for creating musical melody based on user’s physiological index,” in Proc. Joint 4th Int. Conf. on Soft Computing and Intelligent Systems and 9th Int. Symp. on advanced Intelligent Systems, Nagoya, pp. 247-252, 2008.
-  Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, “Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use,” Circulation, Vol.93, pp. 1043-1065, 1996.
-  A. J. Pappano, “Section IV: The Cardiovascular System,” in Berne and Levy Physiology (6th ed.), B. M. Koeppen and B. A. Stanton (Eds.), 2008, pp. 287-414, 2008.
-  J. B. Carter, E. W. Banister, and A. P. Blaber, “Effect of Endurance Exercise on Autonomic Control of Heart Rate,” Sports Med., Vol.33, No.1, pp. 33-46, 2003.
-  M. Iwanaga, A. Kobayashi, and C. Kawasaki, “Heart rate variability with repetitive exposure to music,” Biological Psychology, Vol.70, pp. 61-66, 2005.
-  J. H. Holland, “Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence,” Ann Arbor, MI: The University ofMichigan Press, 1975.
-  D. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning,” Reading, MA: Addison-Wesley Professional, 1989.
-  Y. Yoshida, K. Yokoyama, and N. Ishii, “Realtime continuous assessment method for mental and physiological condition using heart rate variability,” IEEJ Trans. on Electronics, Information and Systems, Vol.126, No.12, pp. 1441-1446, 2006 (in Japanese).
-  P. Gomez and B. Danuser, “Affective and physiological responses to environmental noises and music,” Int. J. Psychophysiol., Vol.53, No.2, pp. 91-103, 2004.
-  J. A. Etzel, E. L. Johnsen, J. Dickerson, D. Tranel, and R. Adolphs, “Cardiovascular and respiratory responses during musical mood induction,” Int. J. Psychophysiol., Vol.61, pp. 57-69, 2006.
-  M. Fukumoto, H. Hasegawa, T. Hazama, and T. Nagashima, “Temporal development of heartbeat interval in transition of sound stimuli inducing different relaxation feelings,” in Proc. Int. Conf. Biometrics and Kansei Engineering 2009, Cieszyn, June 25-28, pp. 84-89, 2009.
-  S. Nakashima, Y. Imamura, S. Ogawa, and M. Fukumoto, “Generation of Appropriate User Chord Development Based on Interactive Genetic Algorithm,” in Proc. the Fifth Int. Conf. on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC-2010), Fukuoka, Nov. 4-6, pp. 450-453, 2010.
-  M. Fukumoto, “Interactive Evolutionary Computation Utilizing Subjective Evaluation and Physiological Information as Evaluation Value,” in Proc. IEEE System, Man, and Cybernetics 2010, Istanbul, Oct. 10-13, pp. 2874-2879, 2010.
-  C. E. Osgood, G. J. Suci, and P. Tannenbaum, “The measurement of meaning,” University of Illinois Press, IL, USA, 1957.
-  H. Takagi and D. Pallez, “Paired Comparison-based Interactive Differential Evolution,” in Proc. World Congress on Nature and Biologically Inspired Computing, Coimbatore, pp. 375-380, 2009.
-  R. Storn and K. V. Price, “Differential evolution simple and efficient adaptive scheme for global optimization over continuous spaces,” Institute of Company Secretaries of India, Chennai, Tamil Nadu. Tech. Report TR-95-012, 1995.
-  S. Iwamiya and T. Nakashima, “On the Effectiveness of Using Musical Chords for Auditory Signals,” The Japanese Journal of Ergonomics, Vol.45, No.6, pp. 329-335, 2009 (in Japanese).
-  M. Unehara and T. Onisawa, “Music Composition by Interaction between Human and Computer,” New Generation Computing, Vol.23, pp. 181-191, 2005.
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