An approach of genetic programming for music emotion classification

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.

[1]  J. Stephen Downie,et al.  Exploring Mood Metadata: Relationships with Genre, Artist and Usage Metadata , 2007, ISMIR.

[2]  José Ranilla,et al.  Scoring and selecting terms for text categorization , 2005, IEEE Intelligent Systems.

[3]  Marc Leman,et al.  Content-Based Music Information Retrieval: Current Directions and Future Challenges , 2008, Proceedings of the IEEE.

[4]  Yan Huang,et al.  Document Categorization with Entropy Based TF/IDF Classifier , 2009, 2009 WRI Global Congress on Intelligent Systems.

[5]  Xuan Zhu,et al.  An integrated music recommendation system , 2006, IEEE Transactions on Consumer Electronics.

[6]  M. Salama,et al.  SVM classification of contaminating particles in liquid dielectrics using higher order statistics of electrical and acoustic PD measurements , 2007, IEEE Transactions on Dielectrics and Electrical Insulation.

[7]  Youngmoo E. Kim,et al.  MoodSwings: A Collaborative Game for Music Mood Label Collection , 2008, ISMIR.

[8]  Yi-Hsuan Yang,et al.  Music emotion classification: a fuzzy approach , 2006, MM '06.

[9]  Lie Lu,et al.  Automatic mood detection from acoustic music data , 2003, ISMIR.

[10]  T. Eerola,et al.  A comparison of the discrete and dimensional models of emotion in music , 2011 .

[11]  Hiromasa Fujihara,et al.  A Modeling of Singing Voice Robust to Accompaniment Sounds and Its Application to Singer Identification and Vocal-Timbre-Similarity-Based Music Information Retrieval , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[12]  Janto Skowronek,et al.  A Demonstrator for Automatic Music Mood Estimation , 2007, ISMIR.

[13]  S. Yoo,et al.  Support Vector Machine Based Arrhythmia Classification Using Reduced Features , 2005 .

[14]  R. Thayer.  The biopsychology of mood and arousal , 1989 .

[15]  Mark D. Hanes,et al.  Acoustic-to-phonetic mapping using recurrent neural networks , 1994, IEEE Trans. Neural Networks.

[16]  S. Nadarajah,et al.  An Explicit Selection Intensity of Tournament Selection-Based Genetic Algorithms , 2008, IEEE Transactions on Evolutionary Computation.

[17]  Yueting Zhuang,et al.  Popular music retrieval by detecting mood , 2003, SIGIR.

[18]  Tao Li,et al.  A comparative study on content-based music genre classification , 2003, SIGIR.

[19]  Moshe Ben-Bassat,et al.  On the Sensitivity of the Probability of Error Rule for Feature Selection , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Wolfgang Nejdl,et al.  Music Mood and Theme Classification - a Hybrid Approach , 2009, ISMIR.

[21]  Tao Li,et al.  Content-based music similarity search and emotion detection , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[22]  Yi-Hsuan Yang,et al.  A Regression Approach to Music Emotion Recognition , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[23]  Chia-Hung Yeh,et al.  An efficient emotion detection scheme for popular music , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[24]  Jee-Hyong Lee,et al.  An Approach to Extract Informative Rules for Web Page Recommendation by Genetic Programming , 2012, IEICE Trans. Commun..

[25]  Alessandro Lameiras Koerich,et al.  Automatic music genre classification using ensemble of classifiers , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[26]  Jee-Hyong Lee,et al.  Feature selection for heavy rain prediction using genetic algorithms , 2012, The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems.

[27]  P. Laukka,et al.  Similar patterns of age-related differences in emotion recognition from speech and music , 2007 .

[28]  John R. Koza,et al.  Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems , 1990 .

[29]  Nikhil R. Pal,et al.  Genetic programming for simultaneous feature selection and classifier design , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[30]  Yi-Hsuan Yang,et al.  Prediction of the Distribution of Perceived Music Emotions Using Discrete Samples , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[31]  Tao Li,et al.  Toward intelligent music information retrieval , 2006, IEEE Transactions on Multimedia.

[32]  Seungmin Rho,et al.  SVR-based music mood classification and context-based music recommendation , 2009, ACM Multimedia.

[33]  Elliot Saltzman,et al.  Retrieving Tract Variables From Acoustics: A Comparison of Different Machine Learning Strategies , 2010, IEEE Journal of Selected Topics in Signal Processing.

[34]  Pedro E. López-de-Teruel,et al.  Nonlinear kernel-based statistical pattern analysis , 2001, IEEE Trans. Neural Networks.

[35]  Michael Van Valkenburgh Associates.  Carnegie Mellon University, School of Computer Science Complex Landscaping , 2009 .

[36]  Anssi Klapuri,et al.  Music Tempo Estimation With $k$-NN Regression , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[37]  Fabian Mörchen,et al.  Modeling timbre distance with temporal statistics from polyphonic music , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[38]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[39]  Tuomas Eerola,et al.  Generalizability and Simplicity as Criteria in Feature Selection: Application to Mood Classification in Music , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[40]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[41]  George Tzanetakis,et al.  Musical genre classification of audio signals , 2002, IEEE Trans. Speech Audio Process..

[42]  Huan Liu,et al.  Incremental Feature Selection , 1998, Applied Intelligence.