Rough Sets Applied to Mood of Music Recognition
Bożena Kostek, Magda Plewa
Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 71–78 (2016)
Abstract. With the growth of accessible digital music libraries over the past decade, there is a need for research into automated systems for searching, organizing and recommending music. Mood of music is considered as one of the most intuitive criteria for listeners, thus this work is focused on the emotional content of music and its automatic recognition. The research study presented in this work contains an attempt to music emotion recognition including audio parameterization and rough sets. A music set consisting of 154 excerpts from 10 music genres was evaluated in the listening experiment. This may be treated as a ground truth. The results achieved indicated a strong correlation between subjective results and objective descriptors and on that basis a vector of parameters related to mood of music was created. On the other hand, rough set-based processing was applied to derive reducts containing the most promising features in the context of mood recognition, as well as confusion matrices of the mood recognition. Both approaches indicate strong relationship between objective descriptors and subjective evaluation of mood of music.
- D. J. Levitin, This Is Your Brain on Music: The Science of a Human Obsession, London, Grove/Atlantic, 2008.
- M. A. Casey, R. Veltkamp, M. Goto, M. Leman, C. Rhodes, M. Slaney, Content-Based Music Information Retrieval: Current Directions and Future Challenges, Proceedings of the IEEE, vol. 96, 4, pp. 668-696, April 2008.
- M. Purgina, A. Kuznetsov, E. Pyshkin, An Approach for Developing a Mobile Accessed Music Search Integration Platform, Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, pp. 267–273, 2013.
- Z. Pawlak, "Rough sets", International Journal of Computer & Information Sciences, vol. 11, no. 5, 341-356, 1982.
- A. Skowron, L. Polkowski (ed.), "Rough Sets" in Knowledge Discovery vol. 1 and 2, Physica Verlag, Heidelberg, 1998.
- J. G. Bazan, M. S. Szczuka, J. Wróblewski, "A new version of rough set exploration system", Third International Conference on Rough Sets and Current Trends in Computing RSCTC, volume 2475, Lecture Notes in Artificial Intelligence, Malvern, PA, Springer-Verlag, pp. 397-404, October 14-16, 2002.
- J. Wróblewski, "Covering with Reducts – A Fast Algorithm for Rule Generation", Proceeding of RSCTC’98, LNAI 1424, Springer Verlag, Berlin, pp. 402–407, 1998.
- B. Kostek, M. Plewa, "Parametrization and correlation analysis applied to music mood classification", International J. of Computational Intelligence Studies, vol. 2, no. 1, pp. 4-25, 2013.
- M. Plewa, "Automatic mood indexing of music excerpts based on correlation between subjective evaluation and feature vector", Doctoral Thesis, Gdańsk University of Technology. Faculty of Electronics, Telecommunications and Informatics, March 2016.
- M. Plewa, B. Kostek, "Creating Mood Dictionary Associated with Music, 132 Audio Eng. Soc. Convention", Paper no. 8607, Budapest, April 26-2, 2012.
- M. Plewa, B. Kostek, "Multidimensional Scaling Analysis Applied to Music Mood Recognition", 134th Audio Eng. Soc. Convention, Paper no. 8876, Rome, May 4-7, 2013.
- M. Plewa, B. Kostek, "Music Mood Visualization Using Self-Organizing Maps", Archives of Acoustics, vol. 50, no. 4, 2015, http://dx.doi.org/10.1515/aoa-2015-0051
- R. E. Thayer, The Biopsychology of Mood and Arousal, Oxford University Press, 1989.
- J. A. Russel, "A circumplex model of affects", Journal of personality and Social Psychology, 39, pp. 1161-1178, 1980.
- B. Brinker, Dinther R., Skowronek J., "Expressed music mood classification compared with valence and arousal ratings", EURASIP J. Audio, Speech, and Music Processing, 1, 2012, http://link.springer.com/journal/13636/2012/1/page/1, access 6.10.2015.
- K. Hevner, "The affective value of pitch and tempo in music", American Journal of Psychology, vol. 49, pp. 621-630, 1937.
- B. Kostek, "Content-Based Approach to Automatic Recommendation of Music", 131st Audio Eng. Soc. Convention, Paper No: 8506, New York, October 21-23, 2011.
- B. Kostek, A. Kupryjanow, P. Żwan, W. Jiang, Z. Ras, M. Wojnarski, J. Swietlicka, "Report of the ISMIS 2011 Contest: Music Information Retrieval", Foundations of Intelligent Systems, ISMIS 2011.
- P. Hoffmann, B. Kostek, "Music Data Processing and Mining in Large Databases for Active Media", Active Media Technology, LNCS, vol. 8610, pp. 85 – 95. Springer, 2014.
- Hoffmann P., Kostek B., Bass Enhancement Settings in Portable Devices Based on Music Genre Recognition; J. Audio Eng. Soc., vol. 63, no. 12, pp. 980 - 989, 12.2015, http://dx.doi.org/10.17743/jaes.2015.0087.
- B. Kostek, A. Kaczmarek, "Music Recommendation Based on Multidimensional Description and Similarity Measures, Fundamenta Informaticae, pp. 1001–1017, DOI 10.3233/FI-2012-0000, 2013.
- B. Kostek, P. Hoffmann, A. Kaczmarek, P. Spaleniak, "Creating a Reliable Music Discovery and Recommendation System", Intelligent Tools for Building a Scientific Information Platform: From Research to Implementation, Springer Verlag, 2013.
- B. Kostek, "Music Information Retrieval in Music Repositories", Chapter 17, in: Rough Sets and Intelligent Systems (Skowron A., Suraj Z., Eds.), vol. 1, ISRL, 42, pp. 463–489, Springer Verlag, Berlin Heidelberg, 2013.
- A. Rosner, F. Weninger, B. Schuller, M. Michalak, B. Kostek, "Influence of Low-Level Features Extracted from Rhythmic and Harmonic Sections on Music Genre Classification", International Conference on Man-Machine Interactions, pp. 467-473, 2013.
- A. Rosner, B. Schuller, and B. Kostek, “Classification of Music Genres Based on Music Separation into Harmonic and Drum Components,” Archives of Acoustics, vol. 39, no. 4, pp. 629–638 (2014), http://dx.doi.org/10.2478/aoa-2014-0068.
- O. Lartillot, "MIRtoolbox 1.4: User’s Manual", Finnish Centre of Excellence in Interdisciplinary Music Research Swiss Center for Affective Sciences, 2012.
- A. Rauber, M. Frühwirth, "Automatically Analyzing and Organizing Music Archives", 5th European Conference on Research and Advanced Technology for Digital Libraries, Springer, London 2001.