Submit Manuscript

Journal of Applied Sciences

Year: 2015 | Volume: 15 | Issue: 2 | Page No.: 289-294
Research Article

Lyrics-Based Genre Classification Using Variant tf-idf Weighting Schemes

Teh Chao Ying
Department of Multimedia, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang, 43400, Malaysia
Shyamala Doraisamy
Department of Multimedia, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang, 43400, Malaysia
Lili Nurliyana Abdullah
Department of Multimedia, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang, 43400, Malaysia
Music documents are often classified based on genre and mood. In recent years, features from lyrics text have been used for classification of musical documents and the feasibility of lyrics features to classify musical documents has been shown. In this study an approach to lyrics based musical genre classification was presented which utilizing mood information. From the analysis of the lyrics text in the data collection, correlation of terms between genre and mood was observed. Based on this correlation of terms, new weighting equation with combine weights from genre and mood was introduced and implemented in two different ways. Ten musical genre and mood categories were selected respectively based on a summary from the literature. Musical genre classification experiments were performed using a test collection consists of 1000 English songs. To confirm present approach can improve the genre classification, experiments were conducted using similar weighting metric from previous study. Experimental results with new weighting equation reveal improvement in musical genre classification.
PDF Fulltext XML References Citation

Leave a Comment

Your email address will not be published. Required fields are marked *

Article Trend

Total views 3639

References

  1. Berenzweig, A., B. Logan, D.P.W. Ellis and B. Whitman, 2003. A large-scale evaluation of acoustic and subjective music similarity measures. Proceedings of the 4th International Society for Music Information Retrieval Conference, October 27-30, 2003, Baltimore, Maryland, USA.
  2. Brilis, S., E. Gkatzou, A. Koursoumis, K. Talvis, K. Kermanidis and I. Karydis, 2012. Mood Classification Using Lyrics and Audio: A Case-Study in Greek Music. In: Artificial Intelligence Applications and Innovations, Iliadis, L., I. Maglogiannis, H. Papadopoulos, K. Karatzas and S. Sioutas (Eds.). Springer, Boston, USA., ISBN: 9783642334115, pp: 421-430.
  3. Hu, X., J. Downie and A. Ehmann, 2009. Lyric text mining in music mood classification. Proceedings of the 10th International Society for Music Information Retrieval Conference, October 26-30, 2009, Kobe, Japan, pp: 411-416.
  4. Hu, X. and J.S. Downie, 2010. Improving mood classification in music digital libraries by combining lyrics and audio. Proceedings of the 10th Annual Joint Conference on Digital Libraries, June 21-25, 2010, Gold Coast, Australia, pp: 159-168.
    CrossRef
  5. Hu, X. and J.S. Downie, 2010. When lyrics outperform audio for music mood classification: A feature analysis. Proceedings of the 11th International Society for Music Information Retrieval Conference, August 9-13, 2010, Utrecht, Netherlands, pp: 619-624.
    Direct Link
  6. Kim, M. and H.C. Kwon, 2011. Lyrics-based emotion classification using feature selection by partial syntactic analysis. Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence, November 7-9, 2011, Boca Raton, FL., USA., pp: 960-964.
    CrossRef
  7. Kumar, V. and S. Minz, 2013. Mood classification of lyrics using sentiwordnet. Proceedings of the International Conference on Computer Communication and Informatics, January 4-6, 2013, Coimbatore, Tamil Nadu, India, pp: 1-5.
  8. Laurier, C., J. Grivolla and P. Herrera, 2008. Multimodal music mood classification using audio and lyrics. Proceedings of the 7th International Conference on Machine Learning and Applications, December 11-13, 2008, San Diego, CA., USA., pp: 688-693.
    CrossRef
  9. Laurier, C., O. Meyers, J. Serra, M. Blech, P. Herrera and X. Serra, 2010. Indexing music by mood: Design and integration of an automatic content-based annotator. Multimedia Tools Appl., 48: 161-184.
    CrossRefDirect Link
  10. Li, T. and G. Tzanetakis, 2001. Automatic musical genre classification of audio signals. Proceedings of the 2nd International Society for Music Information Retrieval Conference, October 15-17, 2001, Indiana, USA.
  11. Lin, Y.C., Y.H. Yang, H.H. Chen, I.B. Liao and Y.C. Ho, 2009. Exploiting genre for music emotion classification. Proceedings of the IEEE International Conference on Multimedia and Expo, June 28-July 3, 2009, New York, pp: 618-621.
    CrossRef
  12. Manning, C.D., P. Raghavan and H. Schutze, 2008. An Introduction to Information Retrieval. Cambridge University Press, USA., ISBN-13: 9780521865715, Pages: 482.
  13. Mayer, R., R. Neumayer and A. Rauber, 2008. Combination of audio and lyrics features for genre classification in digital audio collections. Proceedings of the 16th ACM International Conference on Multimedia, October 26-31, 2008, Vancouver, Canada, pp: 159-168.
    CrossRef
  14. Mayer, R., R. Neumayer and A. Rauber, 2008. Rhyme and style features for musical genre classification by song lyrics. Proceedings of the 9th International Conference on Music Information Retrieval, September 14-18, 2008, Philadelphia, PA., USA., pp: 337-342.
  15. Mayer, R. and A. Rauber, 2010. Multimodal Aspects of Music Retrieval: Audio, Song Lyrics-and Beyond? In: Advances in Music Information Retrieval, Ras, Z. and A. Wieczorkowska (Eds.). Springer, New York, USA., ISBN-13: 9783642116735, pp: 333-363.
  16. Neumayer, R. and A. Rauber, 2007. Integration of text and audio features for genre classification in music information retrieval. Proceedings of the 29th European Conference on IR Research, April 2-5, 2007, Rome, Italy, pp: 724-727.
  17. Oh, S., M. Hahn and J. Kim, 2013. Music mood classification using intro and refrain parts of lyrics. Proceedings of the International Conference on Information Science and Applications, June 24-26, 2013, Suwon, South Kore, pp: 1-3.
    CrossRef
  18. Tzanetakis, G. and P. Cook, 2002. Musical genre classification of audio signals. IEEE Trans. Speech Audio Process., 10: 293-302.
    CrossRef
  19. Yang, D. and W.S. Lee, 2004. Disambiguating music emotion using software agents. Proceedings of the 5th International Society for Music Information Retrieval Conference, October 10-14, 2004, Barcelona, Spain.
  20. Van Zaanen, M. and P. Kanters, 2010. Automatic mood classification using Tf * IDF based on lyrics. Proceedings of the 11th International Society for Music Information Retrieval Conference, August 9-13, 2010, Utrecht, Netherlands, pp: 75-80.

Keywords