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Algorithm based Classification of Songs


Affiliations
1 Department of Electronics and Communication Engineering, National Institute of Technology Kurukshetra, India
2 Department of Electrical and Electronics Engineering, National Institute of Technology Delhi, India
     

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The emotional content perceived from music has great impact on human beings. Research related to music is attaining more and more recognition not only in the field of musicology and psychology but also getting attention of engineers and doctors. The categorization of music can be carried out by considering various attributes such as genres, emotional content, mood, instrumental etc. In this work Hindi music signals belonging to different genres are categorized in four quadrants belonging to Arousal-Valence (AV) plane of emotion categorization i.e. Positive Valence-Positive Arousal (PV-PA), Positive Valence- Negative Arousal (PV-NA), Negative Valence-Positive Arousal (NV-PA) and Negative Valence-Negative Arousal (NV-NA). Features related to music are calculated by using MIR toolbox and the classification techniques used are K-Nearest Neighbor (K-NN), Naive Bayes (NB) and Support Vector Machine (SVM). In this work authors make use of two types of annotation techniques i.e. Subject Based Annotation (SBA) and Algorithm Based Annotation (ABA). The accuracy, precision and recall are considered as evaluation parameter in this work. The evaluation parameters for all the classification techniques using both the annotation methods are compared in the proposed work. Results reveal that SVM classifier outperforms other two classifiers in terms of the parameters considered for both SBA and ABA and it has also been proved that algorithmic annotation outperforms subjective annotation.

Keywords

Music Emotion Recognition, Human Computer Interaction, MIR Toolbox, Arousal, Valence.
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  • S. Shandilya and P. Rao, “Detection of the Singing Voice in Musical Audio”, Proceedings of 114th Convention of Audio Engineering Society, pp. 1-6, 2003.

  • X. Hu, “A Framework for Evaluating Multimodal Music Mood Classification”, Journal of the Association for Information Science and Technology, Vol. 68, No. 2, pp. 273-285, 2017.

  • Y.H. Yang, Y.C. Lin, Y.F. Su and H.H. Chen, “A Regression Approach to Music Emotion Recognition”, IEEE Transactions on Audio Speech and Language Processing, Vol. 16, No. 2, pp. 448-457, 2008.

  • X. Hu and Y.H. Yang, “The Mood of Chinese Pop Music: Representation and Recognition Xiao”, Journal of the Association for Information Science and Technology, Vol. 68, No. 8, pp. 1899-1910, 2017.

  • V. Hampiholi, “A Method for Music Classification based on Perceived Mood Detection for Indian Bollywood Music”, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Vol. 6, No. 12, pp. 507-514, 2012.

  • J. Lee and J. Nam, “Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging”, IEEE Signal Processing Letters, Vol. 24, No. 8, pp. 1208-1212, 2017.

  • Y.H. Yang, Y.F. Su, Y.C. Lin and H.H. Chen, “Music Emotion Recognition”, CRC Press, 2011.

  • Y.H. Yang and H.H. Chen, “Ranking-based Emotion Recognition for Music Organization and Retrieval”, IEEE Transactions on Audio Speech and Language Processing, Vol. 19, No. 4, pp. 762-774, 2011.

  • A. Shakya, B. Gurung, M.S. Thapa and M. Rai, “Music Classification based on Genre and Mood”, Proceedings of International Conference on Computational Intelligence, Communications and Bussiness Analytics, pp. 168-183, 2017.

  • X. Hu and J.S. Downie, “Exploring Mood Metadata: Relationships with Genre, Artist and usage Metadata”, Proceedings of International Conference on Music Information Retrieval, pp. 23-27, 2007.

  • X. Hu, J. S. Downie, C. Laurier, M. Bay and A.F. Ehmann, “The 2007 MIREX Audio Mood Classification Task: Lessons Learned”, Proceedings of International Conference on Music Information Retrieval, pp. 462-467, 2008.

  • S.O. Karl, F. Macdorman and C.C. Ho, “Automatic Emotion Prediction of Song Excerpts: Index Construction, Algorithm Design, and Empirical Comparison”, Journal of New Music Research, Vol. 36, No. 4, pp. 281-299, 2007.

  • T. Li and M. Ogihara, “Detecting Emotion in Music”, Proceedings of International Conference on Music Information Retrieval, pp. 239-240, 2003.

  • D. Huron, “Perceptual and Cognitive Applications in Music Information Retrieval”, Proceedings of International Conference on Music Information Retrieval, pp. 23-25, 2000.

  • D. Huron, “Sweet Anticipation: Music and the Psychology of Expectation”, MIT Press, 2006.

  • A. Gabrielsson,” Emotion Perceived and Emotion Felt: Same or Different”, Musicae Scientiae, Vol. 5, No. 1, pp. 123-147, 2002.

  • S. Hallam, I. Cross and M. Thaut, “The Oxford Handbook of Music Psychology”, Oxford University Press, 2008.

  • K. Hevner, “Expression in Music: A Discussion of Experimental Studies and Theories”, Psychological Review, Vol. 48, No. 2, pp. 186-204, 1935.

  • K. Hevner, “Experimental Studies of the Elements of Expression in Music”, American Journal of Psychology, Vol. 48, No. 2, pp. 246-268, 1936

  • P. Ekman, “An Argument for Basic Emotions”, Cognition and Emotion, Vol. 6, No. 3, pp. 169-200, 1992.

  • R. Plutchik, “Emotion A Psychoevolutionary Synthesis”, Harper and Row, 1980.

  • A. Mehrabian and J.A. Russell, “An Approach to Environmental Psychology”, MIT Press, 1974.

  • J.A. Russell, “A Circumplex Model of Affect”, Journal of Personality and Social Psychology, Vol. 39, No. 6, pp. 1161-1178, 1980.

  • J.A. Russell, “Core Affect and the Psychological Construction of Emotion”, Psychological Review, Vol. 110, No. 1, pp. 145-172, 2003.

  • R.E. Thayer, “The Biopsychology of Mood and Arousal”, Oxford University Press, 1989.

  • E. Bigand, S. Vieillard, F. Madurell, J. Marozeau and A. Dacquet, “Multidimensional Scaling of Emotional Responses to Music: The Effect of Musical Expertise and of the Duration of the Excerpts”, Cognition and Emotion, Vol. 19, No. 8, pp. 1113-1139, 2005.

  • J. Fontaine, K. Scherer, E. Roesch and P. Ellsworth, “The World of Emotions is not Two Dimensional”, Psychological Science, Vol. 18, No. 12, pp. 1050-1057, 2007.

  • O. Lartillot and P. Toiviainen, “MIR in MATLAB (II): A Toolbox for Musical Feature Extraction from Audio”, Proceedings of International Conference on Music Information Retrieval, pp. 127-130, 2007.

  • D. Cabrera, “Psysound: A Computer Program for Psycho-Acoustical Analysis”, Proceedings of International Conference on Australian Acoustic Society, pp. 47-54, 2007.

  • G. Tzanetakis, “MARSYAS submissions to MIREX 2007”, Proceedings of International Conference on Music Information Retrieval Evaluation eXchange, pp. 1-6, 2007.

  • A. Sen and M. Srivastava, “Regression Analysis: Theory, Methods, and Applications”, Springer, 1990.

  • F. Sebastiani, “Machine Learning in Automated Text Categorization”, ACM Computing Surveys, Vol. 34, No. 1, pp. 1-47, 2002.

  • C. Laurier and P. Herrera, “Audio Music Mood Classification using Support Vector Machine”, Proceedings of International Conference on Music Information Retrieval Evaluation eXchange, pp. 501-507, 2007.

  • M.L. Zhang and Z.H. Zhou, “ML-KNN: A Lazy Learning Approach to Multi-Label Learning”, Pattern Recognition, Vol. 40, No. 7, pp. 2038-2048, 2007.

  • K. Trohidis, G. Tsoumakas, G. Kalliris and I. Vlahavas, “Multi-Label Classification of Music into Emotions”, Proceedings of International Conference on Music Information Retrieval, pp. 325-330, 2008.

  • G. Peeters, “A Generic Training and Classification System for MIREX08 Classification Tasks: Audio Music Mood, Audio Genre, Audio Artist and Audio Tag”, Proceedings of International Symposium on Music Information Retrieval, pp. 1-6, 2008.

  • Q. Pu and G.W. Yang, “Short-Text Classification based on ICA and LSA”, Proceedings of International Conference on Advances in Neural Networks, pp. 265-270, 2006.

  • K. Mannepalli, P.N. Sastry and M. Suman, “Analysis of Emotion Recognition System for Telugu using Prosodic and Formant Features”, Proceedings of International Conference on Speech Language Processing and Human-Machine Communications, pp. 137-144, 2018.

  • N. Sebe, M.S. Lew, I. Cohen, A. Garg and T.S. Huang, “Emotion Recognition using a Cauchy Naive Bayes Classifier”, Proceedings of International Conference on Pattern Recognition, pp. 1-15, 2002.

  • Y. Feng, Y. Zhuang and Y. Pan, “Popular Music Retrieval by Detecting Mood”, Proceedings of 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 375-376, 2009.

  • W. Muyuan, Z. Naiyao, Z. Hancheng, M. Wang, N. Zhang and H. Zhu, “User-Adaptive Music Emotion Recognition”, Proceedings of 7th International Conference on Signal Processing, pp. 1352-1355, 2004.

  • M. Ogihara, “Content-Based Music Similarity Search and Emotion Detection”, Proceedings of IEEE International Conference on Acoustics Speech and Signal Processing, pp. 505-508, 2004..

  • L. Lu, D. Liu and H.J. Zhang, “Automatic Mood Detection and Tracking of Music Audio Signals”, IEEE Transactions on Audio, Speech, and Language Processing, Vol. 14, No. 1, pp. 5-18, 2006.

  • P. Saari, T. Eerola and O. Lartillot, “Generalizability and Simplicity as Criteria in Feature Selection: Application to Mood Classification in Music”, IEEE Transactions on Audio, Speech, and Language Processing, Vol. 19, No. 6, pp. 1802-1812, 2011.

  • B. Den Brinker, R. Van Dinther and J. Skowronek, “Expressed Music Mood Classification Compared with Valence and Arousal Ratings”, EURASIP Journal on Audio, Speech, and Music Processing, Vol. 2012, No. 1, pp. 1-14, 2012.

  • J. Wang, Y. Yang, H. Wang and S. Jeng, “Personalized Music Emotion Recognition Via Model Adaptation”, Proceedings of Asia Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 3-6, 2012.

  • K. Markov and T. Matsui, “Music Genre and Emotion Recognition Using Gaussian Processes”, IEEE Access, Vol. 2, pp. 688-697, 2014.

  • H. Ahsan, V. Kumar and C.V. Jawahar, “Multi-Label Annotation of Music”, Proceedings of 8th International Conference on Advances in Pattern Recognition, pp. 4-7, 2015.

  • J.C. Wang, Y.S. Lee, Y.H. Chin, Y.R. Chen and W.C. Hsieh, “Hierarchical Dirichlet Process Mixture Model for Music Emotion Recognition”, IEEE Transactions on Affective Computing, Vol. 6, No. 3, pp. 261-271, 2015.

  • X. Hu and Y.H. Yang, “Cross-Dataset and Cross-Cultural Music Mood Prediction: A Case on Western and Chinese Pop Songs”, IEEE Transactions on Affective Computing, Vol. 8, No. 2, pp. 228-240, 2017.

  • S. Mo and J. Niu, “A Novel Method based on OMPGW Method for Feature Extraction in Automatic Music Mood Classification”, IEEE Transactions on Affective Computing, pp. 1, 2017.

  • B.G. Patra, D. Das and S. Bandyopadhyay, “Multimodal mood classification of Hindi and Western songs”, Journal of Intelligent Information Systems, Vol. 51, No. 8, pp. 1-18, 2018.

  • D. Chaudhary, N.P. Singh and S. Singh, “Genre based Classification of Hindi Songs”, Proceedings of 9th International Conf. on Innovations in Bio-Inspired Computations and Applications, pp. 15-19, 2019.

  • P.K. Jao and Y.H. Yang, “Music Annotation and Retrieval using Unlabeled Exemplars: Correlation and Sparse Codes”, IEEE Signal Processing Letters, Vol. 22, No. 10, pp. 1771-1775, 2015.

  • T. Fawcett, “An Introduction to ROC Analysis”, Pattern Recognition Letters, Vol. 27, No. 8, pp. 861-874, 2006.


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  • Algorithm based Classification of Songs

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Authors

Deepti Chaudhary
Department of Electronics and Communication Engineering, National Institute of Technology Kurukshetra, India
Niraj Pratap Singh
Department of Electronics and Communication Engineering, National Institute of Technology Kurukshetra, India
Sachin Singh
Department of Electrical and Electronics Engineering, National Institute of Technology Delhi, India

Abstract


The emotional content perceived from music has great impact on human beings. Research related to music is attaining more and more recognition not only in the field of musicology and psychology but also getting attention of engineers and doctors. The categorization of music can be carried out by considering various attributes such as genres, emotional content, mood, instrumental etc. In this work Hindi music signals belonging to different genres are categorized in four quadrants belonging to Arousal-Valence (AV) plane of emotion categorization i.e. Positive Valence-Positive Arousal (PV-PA), Positive Valence- Negative Arousal (PV-NA), Negative Valence-Positive Arousal (NV-PA) and Negative Valence-Negative Arousal (NV-NA). Features related to music are calculated by using MIR toolbox and the classification techniques used are K-Nearest Neighbor (K-NN), Naive Bayes (NB) and Support Vector Machine (SVM). In this work authors make use of two types of annotation techniques i.e. Subject Based Annotation (SBA) and Algorithm Based Annotation (ABA). The accuracy, precision and recall are considered as evaluation parameter in this work. The evaluation parameters for all the classification techniques using both the annotation methods are compared in the proposed work. Results reveal that SVM classifier outperforms other two classifiers in terms of the parameters considered for both SBA and ABA and it has also been proved that algorithmic annotation outperforms subjective annotation.

Keywords


Music Emotion Recognition, Human Computer Interaction, MIR Toolbox, Arousal, Valence.

References