Turkish Music Genre Classification using Audio and Lyrics Features

Year 2017, Volume: 21 Issue: 2, 322 - 331, 06.05.2017

Önder Çoban

https://doi.org/10.19113/sdufbed.88303

Cited By: 4

Abstract

Music Information Retrieval (MIR) has become a popular research area in recent years. In this context, researchers have developed music information systems to find solutions for such major problems as automatic playlist creation, hit song detection, and music genre or mood classification. Meta-data information, lyrics, or melodic content of music are used as feature resource in previous works. However, lyrics do not often used in MIR systems and the number of works in this field is not enough especially for Turkish. In this paper, firstly, we have extended our previously created Turkish MIR (TMIR) dataset, which comprises of Turkish lyrics, by including the audio file of each song. Secondly, we have investigated the effect of using audio and textual features together or separately on automatic Music Genre Classification (MGC). We have extracted textual features from lyrics using different feature extraction models such as word2vec and traditional Bag of Words. We have conducted our experiments on Support Vector Machine (SVM) algorithm and analysed the impact of feature selection and different feature groups on MGC. We have considered lyrics based MGC as a text classification task and also investigated the effect of term weighting method. Experimental results show that textual features can also be effective as well as audio features for Turkish MGC, especially when a supervised term weighting method is employed. We have achieved the highest success rate as 99,12\% by using both audio and textual features together.

Keywords

Music genre classification, Lyrics analysis; Word2vec; Audio classification; Machine learning

References

• [1] McKay, C., Burgoyne, J. A., Hockman, J., Smith, J. B., Vigliensoni, G., Fujinaga, I. 2010. Evaluating the Genre Classification Performance of Lyrical Features Relative to Audio, Symbolic and Cultural Features. ISMIR, 9-13 August, Utrecht, 213-218.
• [2] Sordo, M. 2012. Semantic annotation of music collections: A computational approach. Universitat Pompeu Fabra, Department of Information and Communication Technologies, Doctoral dissertation, 18p, Barcelona.
• [3] Hu, X., Downie, J. S. 2010. Improving mood classification in music digital libraries by combining lyrics and audio. In Proceedings of the 10th annual joint conference on Digital libraries, 21-25 June, Gold Coast, QLD, 159-168
• [4] Ying, T. C., Doraisamy, S., Abdullah, L. N. 2012. Genre and mood classification using lyric features. In Information Retrieval & Knowledge Management (CAMP), 13-15 March, Kuala Lumpur, 260-263.
• [5] Çoban, Ö., Özyer, G. T., 2016. Music genre classification from Turkish lyrics. In 2016 24th Signal Processing and Communication Application Conference (SIU), 16-19 May, Zonguldak, 101-104
• [6] Holzapfel, A., Stylianou, Y. 2009. Rhythmic Similarity in Traditional Turkish Music. In ISMIR, 26-30 October, Kobe, 99-104.
• [7] Alpkoçak, A., Gedik, A. C. 2006. Classification of Turkish songs according to makams by using n grams. In Proceedings of the 15. Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN),Muğla
• [8] Kızrak, M. A., Bayram, K. S., Bolat, B. 2014. Classification of Classic Turkish Music Makams. In Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 23-25 June, Alberobello, 394-397
• [9] Kırmacı, B., Oğul, H. 2015. Author recognition from lyrics. Signal Processing and Communications Applications Conference (SIU), 16-19 May, Malatya, 2489-2492.
• [10] Mayer, R., Neumayer, R., Rauber, A. 2008. Rhyme and Style Features for Musical Genre Classification by Song Lyrics. In ISMIR, 14-18 September, Philadelphia, 337-342.
• [11] Zheng, F., Zhang, G., Song, Z. 2001. Comparison of different implementations of MFCC. Journal of Computer Science and Technology, 16(2001), 582-589.
• [12] Yang, D., Lee, W. S. 2009. Music emotion identification from lyrics. In ISM’09, 14-16 December, San Diego, 624-629
• [13] Mayer, R., Rauber, A. 2010. Multimodal Aspects of Music Retrieval: Audio, Song Lyrics–and Beyond?. In Advances in Music Information Retrieval, 333-363
• [14] Van Zaanen, M., Kanters, P. 2010. Automatic Mood Classification Using TF* IDF Based on Lyrics. In ISMIR, 9-13 August, Utrecht, 75-80.
• [15] Oğul, H., Kırmacı, B. 2016. Lyrics Mining for Music Meta-Data Estimation. In IFIP International Conference on Artificial Intelligence Applications and Innovations. Thessaloniki, 16-18 September, 528-539.
• [16] Yaslan, Y., Cataltepe, Z. 2006. Audio music genre classification using different classifiers and feature selection methods. In 18th International Conference on Pattern Recognition (ICPR’06), 20-24 August, Hong Kong, 573-576
• [17] Dhanaraj, R., Logan, B. 2005. Automatic Prediction of Hit Songs. In ISMIR, 11-15 September, London, 488-491.
• [18] Cataltepe, Z., Yaslan, Y., Sonmez, A. 2007. Music genre classification using MIDI and audio features. EURASIP Journal on Advances in Signal Processing, 2007(2007), 1-8.
• [19] McKay, C., Fujinaga, I. 2008. Combining Features Extracted from Audio, Symbolic and Cultural Sources. In ISMIR, 14-18 September, Philadelphia, 597-602.
• [20] McKay, C., Fujinaga, I., Depalle, P. 2005. jAudio: A feature extraction library. In Proceedings of the International Conference on Music Information Retrieval, 11-15 September, London, 600-3.
• [21] McEnnis, D., McKay, C., Fujinaga, I., Depalle, P. 2006. jAudio: Additions and Improvements. In ISMIR, 8-12 October, Victoria, 385-386.
• [22] Hu, X., Downie, J. S., Ehmann, A. F. 2009 Lyric text mining in music mood classification. American music, 183(2009), 2-209.
• [23] Lin, Y., Lei, H., Wu, J., Li, X. 2015. An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding. PACLIC 29, 30 October- 1 November, Shanghai, 258-266.
• [24] Akın, A. A., Akın, M. D. 2007. Zemberek, an open source NLP framework for Turkic languages. Structure, 10(2007), 1-5.
• [25] Çoban, Ö., Özyer, G. T., 2016. Sentiment classification for Turkish Twitter feeds using LDA. In 2016 24th Signal Processing and Communication Application Conference (SIU), 16-19 May, Zonguldak, 129-132.
• [26] Öztürk, M. B., Can, B. 2016. Clustering word roots syntactically. In 2016 24th Signal Processing and Communication Application Conference (SIU), 16-19 May, Zonguldak, 1461-1464
• [27] Joachims, T. 1997. A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. Proceedings of the Fourteenth International Conference on Machine Learning (ICML’97 ), 8-12 July, 143-151.
• [28] Lewis, D. D. 1992. An evaluation of phrasal and clustered representations on a text categorization task. In Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, 21-24 June, Copenhagen, 37-50.
• [29] Kanaris, I., Kanaris, K., Houvardas, I., Stamatatos, E. 2006. Words Vs Characters N-Grams for Anti-Spam Filtering. International Journal on Artificial Intelligence Tools, world Scientific, X(2006), 1-20.
• [30] Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C. 2002. Text classification using string kernels. Journal of Machine Learning Research, 2(2002), 419-444.
• [31] Stamatatos, E., Fakotakis, N., Kokkinakis, G. 2000. Automatic text categorization in terms of genre and author. Computational linguistics, 26(2000), 471-495.
• [32] Mayer, R., Neumayer, R., Rauber, A. 2008. Combination of audio and lyrics features for genre classification in digital audio collections. In Proceedings of the 16th ACM international conference on Multimedia, 26-31 October, Vancouver, 159-168
• [33] Mikolov, T., Chen, K., Corrado, G., Dean, J. 2013. Efficient estimation of word representations in vector space. In 2013 International Conference on Learning Representations (ICLR), 2-4 May, Scottsdale.
• [34] Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., Dean, J. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, 5-10 December, Lake Tahoe, 3111-3119.
• [35] Yuan, Y., He, L., Peng, L., Huang, Z. 2014. A new study based on word2vec and cluster for document categorization. Journal of Computational Information Systems, 10(2014), 9301-9308.
• [36] Salton, G., Wong, A., Yang, C. S. 1975. A vector space model for automatic indexing. Communications of the ACM, 18(1975), 613-620.
• [37] Kansheng, S. H. I., Jie, H. E., Liu, H. T., Zhang, N. T., Song, W. T. 2011. Efficient text classification method based on improved term reduction and term weighting. The Journal of China Universities of Posts and Telecommunications, 18(2011), 131-135.
• [38] Lan, M., Tan, C. L., Su, J., Lu, Y. 2009. Supervised and traditional term weighting methods for automatic text categorization. IEEE transactions on pattern analysis and machine intelligence, 31(2009), 721-735.
• [39] Hall, M. A. 2000. Correlation-based feature selection for discrete and numeric class machine learning. In Proceedings of the Seventeenth International Conference on Machine Learning, 29 June-2 July, Stanford, 359–366.
• [40] Joachims, T. 1998. Text categorization with support vector machines: Learning with many relevant features. In European conference on machine learning, 21-23 April, Chemnitz, 137-142.
• [41] Kotsiantis, S. B., Zaharakis, I., Pintelas, P. 2007. Supervised machine learning: A review of classification techniques. In Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering, 3-24.
• [42] Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I. H. 2009. The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(2009), 10-18.
• [43] Kohavi, R. 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai, 14(1995), 1137-1145.