Abstract
Lyrics-based music genre classification is still understudied within the music information retrieval community. The existing approaches, reported in the literature, only deals with lyrics in the English language. Thus, it is necessary to evaluate if the standard text classification techniques are suitable for lyrics in languages other than English. More precisely, in this work we are interested in analyzing which approach gives better results: a language-dependent approach using stemming and stopwords removal or a language-independent approach using n-grams. To perform the experiments we have created the Nordic music genre lyrics database. The analysis of the experimental results shows that using a language-independent approach with the n-gram representation is better than using a language-dependent approach with stemming. Additional experiments using stylistic features were also performed. The analysis of these additional experiments has shown that using stylistic features combined with the other approaches improve the classification results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Orio, N.: Music retrieval: a tutorial and review. Foundations and Trends in Information Retrieval 1(1), 1–90 (2006)
Mayer, R., Neumayer, R., Rauber, A.: Combination of audio and lyrics features for genre classification in digital audio collections. In: Proceedings of the 16th ACM International Conference on Multimedia, pp. 159–168 (2008)
Mayer, R., Neumayer, R., Rauber, A.: Rhyme and style features for musical genre classification by song lyrics. In: Proceedings of the 9th International Conference on Music Information Retrieval, pp. 337–342 (2008)
Mayer, R., Neumayer, R.: Multi-modal analysis of music: A large-scale evaluation. In: Proceedings of the Workshop on Exploring Musical Information Spaces, pp. 30–35 (2009)
Mayer, R., Rauber, A.: Building ensembles of audio and lyrics features to improve musical genre classification. In: Proceedings of the International Conference on Distributed Framework and Applications, pp. 1–6 (2010)
Mayer, R., Rauber, A.: Musical genre classification by ensembles of audio and lyrics features. In: Proceedings of International Conference on Music Information Retrieval, pp. 675–680 (2011)
Silla Jr., C.N., Koerich, A.L., Kaestner, C.A.A.: Improving automatic music genre classification with hybrid content-based feature vectors. In: Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1702–1707 (2010)
El-Khair, I.A.: Effects of stop words elimination for arabic information retrieval: a comparative study. International Journal of Computing & Information Sciences 4(3), 119–133 (2006)
Yu, B.: An evaluation of text classification methods for literary study. Literary and Linguistic Computing 23(3), 327–343 (2008)
Hu, X., Downie, J.S.: Improving mood classification in music digital libraries by combining lyrics and audio. In: Proceedings of the 10th Annual Joint Conference on Digital Libraries, pp. 159–168 (2010)
Cavnar, W.B., Trenkle, J.M.: N-gram-based text categorization. In: Proceedings of the 3rd Annual Symposium on Document Analysis and Information Retrieval, pp. 161–175 (1994)
Porter, M.F.: An algorithm for suffix stripping. Program: Electronic Library and Information Systems 14, 130–137 (1980)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Porter, M.F.: Snowball: A language for stemming algorithms, http://snowball.tartarus.org/texts/introduction.html
Tokunaga, T., Makoto, I.: Text categorization based on weighted inverse document frequency. Technical report, Tokyo Institute of Technology (1994)
Wu, H., Salton, G.: A comparison of search term weighting: Term relevance vs. inverse document frequency. In: Proceedings of the 4th Special Interest Group on Information Retrieval, pp. 30–39 (1981)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing & Management 24(5), 513–523 (1988)
Fabbri, F.: Browsing music spaces: Categories and the musical mind (1999)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann (2005)
Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods, pp. 185–208 (1999)
Dhanaraj, R., Logan, B.: Automatic prediction of hit songs. In: Proceedings of International Conference on Music Information Retrieval, pp. 488–491 (2005)
Laurier, C., Grivolla, J., Herrera, P.: Multimodal music mood classification using audio and lyrics. In: Proceedings of the 7th International Conference on Machine Learning and Applications, pp. 688–693 (2008)
Koppel, M., Schler, J., Argamon, S.: Computational methods in authorship attribution. Journal of the American Society for Information Science and Technology 60(1), 9–26 (2009)
HaCohen-Kerner, Y., Beck, H., Yehudai, E., Rosenstein, M., Mughaz, D.: Cuisine: Classification using stylistic feature sets and/or name-based feature sets. Journal of the American Society for Information Science and Technology 61, 1644–1657 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
de Lima, A.A., Nunes, R.M., Ribeiro, R.P., Silla, C.N. (2014). Nordic Music Genre Classification Using Song Lyrics. In: Métais, E., Roche, M., Teisseire, M. (eds) Natural Language Processing and Information Systems. NLDB 2014. Lecture Notes in Computer Science, vol 8455. Springer, Cham. https://doi.org/10.1007/978-3-319-07983-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-07983-7_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07982-0
Online ISBN: 978-3-319-07983-7
eBook Packages: Computer ScienceComputer Science (R0)