The role of audio and tags in music mood prediction: A study using semantic layer projection

Proceedings of the 14th International Society for Music Information Retrieval Conference, ISMIR 2013

Volumn , Issue , 2013, Pages 201-206

  Saari, Pasi ^a   Eerola, Tuomas ^a   Fazekas, György ^b   Barthet, Mathieu ^b   Lartillot, Olivier ^a   Sandler, Mark ^b  

^a UNIVERSITY OF JYVÄSKYLÄ   (Finland)

^b QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; INFORMATION RETRIEVAL; SEMANTICS;

AUDIO FEATURES; CIRCUMPLEX MODELS; CONTINUOUS SCALE; LARGE SCALE DATA SETS; LAST.FM; LOW DIMENSIONAL; SEMANTIC LAYER; SEMANTIC TAGS;

AUDIO ACOUSTICS;

EID: 85044951126     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. J. A. Sloboda and P. N. Juslin. Music and Emotion, chapter Psychological Perspectives on Music and Emotion, pages 71–104. Oxford University Press, New York, 2001.
2. Z. Fu, G. Lu, K. M. Ting, and D. Zhang. A survey of audio-based music classification and annotation. IEEE Transactions on Multimedia, 13(2):303 –319, 2011.
3. M. Barthet, G. Fazekas, and M. Sandler. Multidisciplinary perspectives on music emotion recognition: Recommendations for content- and context-based models. In Proc. of the 9th Int. Symposium on Computer Music modelling and Retrieval (CMMR), pages 492–507, 2012.
4. J. A. Russell. A circumplex model of affect. Journal of Personality and Social Psychology, 39(6):1161–1178, 1980.
5. K. R. Scherer. Emotion as a multicomponent process: A model and some cross-cultural data, pages 37–63. CA: Sage, Beverly Hills, 1984.
6. T. Eerola and J. K. Vuoskoski. A review of music and emotion studies: Approaches, emotion models and stimuli. Music Perception, 30(3):307–340, 2012.
7. R. E. Thayer. The Biopsychology of Mood and Arousal. Oxford University Press, New York, USA, 1989.
8. C. Laurier, M. Sordo, J. Serra, and P. Herrera. Music mood representations from social tags. In Proceedings of 10th International Conference on Music Information Retrieval (ISMIR), pages 381–86, 2009.
9. M. Levy and M. Sandler. A semantic space for music derived from social tags. In Proceedings of 8th International Conference on Music Information Retrieval (ISMIR), 2007.
10. P. Saari and T. Eerola. Semantic computing of moods based on tags in social media of music. IEEE Transactions on Knowledge and Data Engineering, In press 2013.
11. P. Saari, M. Barthet, G. Fazekas, T. Eerola, and M. Sandler. Semantic models of mood expressed by music: Comparison between crowd-sourced and curated editorial annotations. In IEEE International Conference on Multimedia and Expo (ICME 2013): International Workshop on Affective Analysis in Multimedia, 2013.
12. D. Turnbull, L. Barrington, D. Torres, and G. Lanckriet. Semantic annotation and retrieval of music and sound effects. IEEE Transactions on Audio, Speech, and Language Processing, 16(2):467–476, 2008.
13. M. I. Mandel and D. PW Ellis. A web-based game for collecting music metadata. Journal of New Music Research, 37(2):151–165, 2008.
14. M. I. Mandel and D. P. Ellis. Multiple-instance learning for music information retrieval. In Proceedings of 9th International Conference of Music Information Retrieval (ISMIR), pages 577–582, 2008.
15. R. Miotto and G. Lanckriet. A generative context model for semantic music annotation and retrieval. IEEE Transactions on Audio, Speech, and Language Processing, 20(4):1096–1108, 2012.
16. T. Bertin-Mahieux, D. Eck, F. Maillet, and P. Lamere. Autotagger: A model for predicting social tags from acoustic features on large music databases. Journal of New Music Research, 37(2):115–135, 2008.
17. P. Saari, T. Eerola, G. Fazekas, and M. Sandler. Using semantic layer projection for enhancing music mood prediction with audio features. In Sound and Music Computing Conference, 2013.
18. J. C. Gower and G. B. Dijksterhuis. Procrustes problems, volume 3. Oxford University Press Oxford, 2004.
19. O. Lartillot and P. Toiviainen. A matlab toolbox for musical feature extraction from audio. In Proceedings of the 10th International Conference on Digital Audio Effects, 2007.
20. S. Wold, M. Sjöström, and L. Eriksson. Pls-regression: a basic tool of chemometrics. Chemometrics and intelligent laboratory systems, 58(2):109–130, 2001.
21. P. Saari, T. Eerola, and O. Lartillot. Generalizability and simplicity as criteria in feature selection: Application to mood classification in music. IEEE Transactions on Speech and Audio Processing, 19(6):1802 –1812, 2011.
22. 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, 16(2):448–457, 2008.