Abstract
In this paper, we suggest a new approach of genetic programming for music emotion classification. Our approach is based on Thayer’s arousal-valence plane which is one of representative human emotion models. Thayer’s plane which says human emotions is determined by the psychological arousal and valence. We map music pieces onto the arousal-valence plane, and classify the music emotion in that space. We extract 85 acoustic features from music signals, rank those by the information gain and choose the top k best features in the feature selection process. In order to map music pieces in the feature space onto the arousal-valence space, we apply genetic programming. The genetic programming is designed for finding an optimal formula which maps given music pieces to the arousal-valence space so that music emotions are effectively classified. k-NN and SVM methods which are widely used in classification are used for the classification of music emotions in the arousal-valence space. For verifying our method, we compare with other six existing methods on the same music data set. With this experiment, we confirm the proposed method is superior to others.
Similar content being viewed by others
References
T. Li and M. Ogihara, “Toward intelligent music information retrieval,” IEEE Trans. on Multimedia, vol. 8, no. 3, pp. 564–574, 2006.
M. A. Casey, R. Veltkamp, M. Goto, M. Leman, C. Rhodes, and M. Slaney, “Content-based music information retrieval: current directions and future challenges,” Proc. of the IEEE, vol. 96, no. 4, pp. 668–696, 2008.
G. Tzanetakis and P. Cook, “Musical genre classification of audio signals,” IEEE Trans. on Speech and Audio Processing, vol. 10, no. 5, pp. 293–302, 2002.
H. Fujihara, M. Goto, T. Kitahara, and H. G. Okuno, “A modeling of singing voice robust to accompaniment sounds and its application to singer identification and vocal-timbre-similarity-based music information retrieval,” IEEE Trans. on Audio, Speech, and Language Processing, vol. 18, no. 3, pp. 638–648, 2010.
X. Zhu, Y.-Y. Shi, H.-G. Kim, and K.-W. Eom, “An integrated music recommendation system,” IEEE Trans. on Consumer Electronics, vol. 52, no. 3, pp. 917–925, 2006.
P. Saari, T. Eerola, and O. Lartillot, “Generalizability and simplicity as criteria in feature selection: application to mood classification in music,” IEEE Trans. on Audio, Speech, and Language Processing, vol. 19, no. 6, pp. 1802–1812, 2011.
Y. -H. Yang, Y. C. Lin, Y. F. Su, and H. H. Chen, “A regression approach to music emotion recognition,” IEEE Trans. on Audio, Speech, and Language Processing, vol. 16, no. 2, pp. 448–457, 2008.
F. Morchen, A. Ultsch, M. Thies, and I. Lohken, “Modeling timbre distance with temporal statistics from polyphonic music,” IEEE Trans. on Audio, Speech, and Language Processing, vol. 14, no. 1, pp. 81–90, 2006.
H. Liu and R. Setiono, “Incremental feature selection,” Journal Applied Intelligence, vol. 9, no. 3, 1998.
M. Robnki-Sikonija and I. Kononenko, “Theoretical and empirical analysis of ReliefF and RReliefF,” Machine Learning Journal, vol. 53, pp. 23–69, 2003.
R. M. Sharkawy, R. S. Mangoubi, T. K. Abdel- Galil, M. M. A. Salama, and R. Bartnikas, “SVM classification of contaminating particles in liquid dielectrics using higher order statistics of electrical and acoustic PD measurements,” IEEE Trans. on Dielectrics and Electrical Insulation, vol. 14, no. 3, pp. 669–678, 2007.
M. H. Song, J. Lee, S. P. Cho, K. J. Lee, and S. K. Yoo, “Support vector machine based arrhythmia classification using reduced features,” International Journal of Control, Automation, and Systems, vol. 3, no. 4, pp. 571–579, 2005.
M. D. Hanes, S. C. Ahalt, and A. K. Krishnamurthy, “Acoustic-to-phonetic mapping using recurrent neural networks,” IEEE Trans. on Neural Networks, vol. 5, no. 4, pp. 659–662, 1994.
A. J. Eronen and A. P. Klapuri, “Music tempo estimation with k-NN regression,” IEEE Trans. on Audio, Speech, and Language Processing, vol. 18, no. 1, pp. 50–57, 2010.
V. Mitra, H. Nam, C. Y. Espy-Wilson, E. Saltzman, and L. Goldstein, “Retrieving tract variables from acoustics: a comparison of different machine learning strategies,” IEEE Journal of Selected Topics in Signal Processing, vol. 4, no. 6, pp. 1027–1045, 2010.
Y. -H. Yang and H. H. Chen, “Prediction of the distribution of perceived music emotions using discrete samples,” IEEE Trans. on Audio, Speech, and Language Processing, vol. 19, no. 7, pp. 2184–2196, 2011.
R. E. Thayer, The Biopsychology of Mood and Arousal, Oxford University Press, NY, 1989.
C. -H. Yeh, H.-H. Lin, and H.-T. Chang, “An efficient emotion detection scheme for popular music,” Proc. of the IEEE International Symposium on Circuits and Systems, pp. 1799–1802, 2009.
P. Laukka and P. N. Juslin, “Similar patterns of age-related differences in emotion recognition from speech and music,” Motivation and Emotion, vol. 31, no. 3, pp. 182–191, 2007.
Y. H. Yang, C. C. Liu, H. H. Chen, Y. H. Yang, C. C. Liu, and H. H Chen, “Music emotion classification: a fuzzy approach,” Proc. of the ACM Multimedia, pp. 81–84, 2006.
E. Montanes, I. Diaz, J. Ranilla, E. F. Combarro, and J. Fernandez, “Scoring and selecting terms for text categorization,” IEEE Intelligent Systems, vol. 20, no. 3, pp. 40–47, 2005.
Y. Lu and Y. Huang “Document categorization with entropy based TF/IDF classifier,” Proc. of the WRI Global Congress on Intelligent Systems, vol. 4, pp. 269–273, 2009.
D. P. Muni, N. R. Pal, and J. Das, “Genetic programming for simultaneous feature selection and classifier design,” IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 36, no. 1, pp. 106–117, 2006.
J. Kim, K. Yoon, and J.-H. Lee, “An approach to extract informative rules for web page recommendation by genetic programming,” IEICE Trans. on Communications, vol. E95-B, no. 05, May 2012.
AllMusic, http://www.allmusic.com/.
T. Li and M. Ogihara, “Content-based music similarity search and emotion detection,” Proc. of the International Conference on Acoustic, Speech, Signal Process, Toulouse, France, pp. 17–21, 2004.
S. M. Rho, B. J. Han, and E. J. Hwang, “SVRbased music mood classification and context-based music recommendation,” Proc. of the ACM International Conference on Multimedia, pp. 713–716, 2009.
K. Bischoff, C. S. Firan, R. Paiu, W. Nejdl, C. Laurier, and M. Sordo, “Music mood and theme classification: a hybrid approach,” Proc. of the ISMIR, pp. 657–662. 2009.
J. Skowronek, M. McKinney, and S. Par, “A demonstrator for automatic music mood estimation,” Proc. of the ISMIR, pp. 345–346, 2007.
Y. E. Kim, E. Schmidt, and L. Emelle, “Moddswings: a collaborative game for music mood label collection,” Proc. of the ISMIR, pp. 231–236, 2008.
T. Li, M. Ogihara and Q. Li, “A comparative study on content-based music genre classification,” Proc. of the SIGIR, pp. 282–289, 2003.
Y. Feng, Y. Zhuang, and Y. Pan, “Popular music retrieval by detecting mood,” Proc. of the SIGIR, pp. 375–376, 2003.
D. Liu, L. Lu, and H. J. Zhang, “Automatic mood detection from acoustic music data,” Proc. of the ISMIR, pp. 81–87, 2003.
E. Schubert, Measurement and Time Series Analysis of Emotion in Music, Ph.D. Thesis, University of New South Wales, 1999.
T. Eerola and J. K. Vuoskoski, “A comparison of the discrete and dimensional models of emotion in music,” Psychology of Music, vol. 39, no. 1, pp. 18–49, 2011.
M. B. Bassat, “On the sensitivity of the probability of error rule for feature selection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. PAMI-2, no. 1, pp. 57–61, January 1990.
A. W. Moore, “Information gain,” School of Computer Science, Carnegie Mellon University, http://www.cs.cmu.edu/~awm/tutorials, 2001.
J. R. Koza, “Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems,” Stanford University Computer Science Department technical report STAN-CS-90-1314, June 1990.
S. Nadarajah, “An explicit selection intensity of tournament selection-based genetic algorithms,” IEEE Trans. on Evolutionary Computation, vol. 12, no. 3, pp. 389–391, 2008.
C. N. Silla, C. A. A. Kaestner, and A. L. Koerich, “Automatic music genre classification using ensemble of classifiers,” Proc. of the IEEE International Conference on Systems, Man and Cybernetics (ISIC), pp. 1687–1692, 2007.
PsySound, http://psysound.wikidot.com/.
A. Ruiz and P. E. Lopez-de-Teruel, “Nonlinear kernel-based statistical pattern analysis,” IEEE Trans. on Neural Networks, vol. 12, no. 1, pp. 16–32, 2001.
V. Vapnik, The Nature of Statistical Learning Theory, Springer Verlag, Heidelberg, December 1995.
J. Lee, J. Kim, J.-H. Lee, I.-H. Cho, J.-W. Lee, K.-H. Park, and J. G. Park, “Feature selection for heavy rain prediction using genetic algorithm,” Proc. of the 6th International Conference on Soft Computing and Intelligent Systems, and the 13th International Symposium on Advanced Intelligent Systems, pp. 830–833, 2012.
X. Hu and J. S. Downine, “Exploring mood metadata: relationships with genre, artist and usage metadata,” Proc. of the ISMIR, pp. 67–72, 2007.
Author information
Authors and Affiliations
Corresponding author
Additional information
Recommended by Editor Young-Hoon Joo.
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2013-0458-000) and IT R&D program of MKE/KEIT (10041244, Smart TV 2.0 Software Platform).
Sung-Woo Bang received his M.S. degree in the Department of Electrical and Computer Engineering from Sungkyunkwan University, Suwon, Korea, in 2011. He is currently a counselor in LG CTS, Seoul, Korea. His current interest fields are web-based inference and intelligent systems.
Jaekwang Kim received his B.S. and M.S. degrees from Sungkyunkwan University, Suwon, Korea in 2004, and 2006, respectively. His research interests include networks, security, and intelligent systems. He is currently a Ph.D. student at the Department of Electrical and Computer. Engineering, Sungkyunkwan University, He received the best presentation paper award at ICUIMC, Suwon, Korea in Jan. 2009.
Jee-Hyong Lee received his M.S. and Ph.D. degrees in Computer Science from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 1995 and 1999, respectively. He was an international fellow at SRI International, California from 2000 to 2001. He has been working as a faculty at Sungkyunkwan University, Suwon, Korea since March 2002. His current interest fields are web intelligence and intelligent information processing.
Rights and permissions
About this article
Cite this article
Bang, SW., Kim, J. & Lee, JH. An approach of genetic programming for music emotion classification. Int. J. Control Autom. Syst. 11, 1290–1299 (2013). https://doi.org/10.1007/s12555-012-9407-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12555-012-9407-7