Extracting emotions from music data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3488 LNAI, Issue , 2005, Pages 456-465

  Wieczorkowska, Alicja ^a   Synak, Piotr ^a   Lewis, Rory ^b   Raś, Zbigniew W ^a,b  

^a POLISH JAPANESE INSTITUTE OF INFORMATION TECHNOLOGY   (Poland)

^b University of North Carolina at Charlotte   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPACT DISKS; COMPUTER MUSIC; CONTENT BASED RETRIEVAL; INTERNET;

AUDIO DATA; AUDIO FILES; CONTENT-BASED SEARCHING;

DATA REDUCTION;

EID: 26944434794     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11425274_47     Document Type: Conference Paper

References (31)

1. Apple: iTunes (2004). http://www.apple.com/itunes/
2. Batlle, E. and Cano, P.: Automatic Segmentation for Music Classification using Competitive Hidden Markov Models. Proceedings of International Symposium on Music Information Retrieval, Plymouth, MA (2000). Available at http://www.iua.upf.es/mtg/publications/ismir2000-eloi.pdf
3. Berger, A.: Error-correcting output coding for text classification. IJCAI'99: Workshop on machine learning for information filtering. Stockholm, Sweden (1999). Available at http://www-2.cs.cmu.edu/aberger/pdf/ecoc.pdf
4. Brown, J. C.: Computer identification of musical instruments using pattern recognition with cepstral coefficients as features. J. Acoust. Soc. of America 105 (1999), 1933-1941
5. Carletta, J.: Assessing agreement on classification tasks: the kappa statistic. Computational Linguistics 22 (2) (1996), 249-254. Available at http://homepages.inf.ed.ac.uk/jeanc/squib.pdf
6. Cross, I.: Music, cognition, culture and evolution. Annals of the New York Academy of Sciences, 930 (2001), 28-42. Available at http://www-ext.mus. cam.ac.uk/ic108/PDF/IRMCNYAS.pdf
7. Dellaert, F., Polzin, T., Waibel, A.: Recognizing Emotion in Speech. Proc. ICSLP 96 3 (1996), 1970-1973.
8. Eronen, A. and Klapuri, A.: Musical Instrument Recognition Using Cepstral Coefficients and Temporal Features. Proc. IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2000, Plymouth, MA (2000). 753-756
9. Fujinaga, I., McMillan, K.: Realtime recognition of orchestral instruments. Proceedings of the International Computer Music Conference (2000) 141-143
10. Herrera, P., Amatriain, X., Batlle, E., and Serra X.: Towards instrument segmentation for music content description: a critical review of instrument classification techniques. In Proc. of International Symposium on Music Information Retrieval ISMIR 2000, Plymouth, MA (2000)
11. Hevner, K.: Experimental studies of the elements of expression in music. American Journal of Psychology 48 (1936) 246-268
12. Huron, D.: Sound, music and emotion: An introduction to the experimental research. Seminar presentation, Society for Music Perception and Cognition Conference. Massachusetts Institute of Technology, Cambridge, MA (1997).
13. Juslin, P., Sloboda, J. (eds.): Music and Emotion: Theory and Research. Series in Affective Science. Oxford University Press (2001)
14. Kaminskyj, I.: Multi-feature Musical Instrument Classifier. MikroPolyphonie 6 (2000). Online journal at http://www.mikropol.net/
15. Kostek, B. and Czyzewski, A.: Representing Musical Instrument Sounds for Their Automatic Classification. J. Audio Eng. Soc. 49(9) (2001). 768-785
16. Kostek, B, Wieczorkowska, A.: Parametric Representation Of Musical Sounds. Archives of Acoustics 22, 1 (1997) 3-26
17. Lavy, M. M.: Emotion and the Experience of Listening to Music. A Framework for Empirical Research. PhD. dissertation, Jesus College, Cambridge (2001).
18. Li, T., Ogihara, M.: Detecting emotion in music. 4th International Conference on Music Information Retrieval ISMIR, Washington, D.C., and Baltimore, MD (2003).
19. Mantaras, R. L. de, Arcos, J. L.: AI and Music. From Composition to Expressive Performance. AI Magazine, Fall 2002 (2002) 43-58
20. Marasek, K.: private communication (2004).
21. Martin, K. D., Kim, Y. E.: Musical instrument identification: A pattern-recognition approach. 136 meeting of the Acoustical Soc. of America, Norfolk, VA (1998)
22. Microsoft Corp.: Windows Media Player (2004). http://www.microsoft.com/
23. Pachet, F.: Beyond the Cybernetic Jam Fantasy: The Continuator. IEEE Computers Graphics and Applications, Jan./Feb. 2004, spec, issue on Emerging Technologies.
24. Pachet, F.: Knowledge Management and Musical Metadata. In: Schwartz, D. (Ed.), Encyclopedia of Knowledge Management. Idea Group (2005).
25. Peeters, G. Rodet, X.: Automatically selecting signal descriptors for Sound Classification. ICMC 2002 Goteborg, Sweden (2002)
26. Pollard, H. F., Jansson, E. V.: A Tristimulus Method for the Specification of Musical Timbre. Acustica 51 (1982) 162-171
27. Tato, R., Santos, R., Kompe, R., Pardo, J. M.: Emotional Space Improves Emotion Recognition. 7th International Conference on Spoken Language Processing ICSLP 2002, Denver, Colorado (2002).
28. Tzanetakis, G., Cook, P.: Marsyas: A framework for audio analysis. Organized Sound 4(3) (2000) 169-175.
29. Widmer, G.: Discovering Simple Rules in Complex Data: A Meta-learning Algorithm and Some Surprising Musical Discoveries. Artificial Intelligence 146(2) (2003)
30. Wieczorkowska, A., Wroblewski, J., Slezak, D., Synak, P.: Application of temporal descriptors to musical instrument sound recognition. Journal of Intelligent Information Systems 21(1), Kluwer (2003), 71-93
31. WordIQ Dictionary (2004). The Internet http://www.wordiq.com/dictionary/