Automatic music mood detection

Details Automatic music mood detection Automatic music mood detection

2006-04-04

2006-04-04

Donels, Jeffrey W (Department: 2837)

Music

Instruments

Electrical musical tone generation

C084S622000

Reexamination Certificate

active

07022907

ABSTRACT:
A system and methods use music features extracted from music to detect a music mood within a hierarchical mood detection framework. A two-dimensional mood model divides music into four moods which include contentment, depression, exuberance, and anxious/frantic. A mood detection algorithm uses a hierarchical mood detection framework to determine which of the four moods is associated with a music clip based on the extracted features. In a first tier of the hierarchical detection process, the algorithm determines one of two mood groups to which the music clip belongs. In a second tier of the hierarchical detection process, the algorithm then determines which mood from within the selected mood group is the appropriate, exact mood for the music clip. Benefits of the mood detection system include automatic detection of music mood which can be used as music metadata to manage music through music representation and classification.

REFERENCES:
patent: 5616876 (1997-04-01), Cluts
patent: 6185527 (2001-02-01), Petkovic et al.
patent: 6225546 (2001-05-01), Kraft et al.
patent: 6545209 (2003-04-01), Flannery et al.
patent: 6657117 (2003-12-01), Weare et al.
patent: 6665644 (2003-12-01), Kanevsky et al.
patent: 2005/0120868 (2005-06-01), Hinman et al.
Liu D. et al., “Form and mood recognition of Johann Strauss's waltz centos,” Chinese Journal of Electronics, Oct. 2003, vol. 12, No. 4, pp. 587-593.
Pinquier, J. et al., “A fusion study in speech/ music classification,” 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. II-17-20.
Liu, C.C. et al., “A singer identification technique for content-based classification of MP3 music objects,” Proceedings of the Eleventh International Conference on Information and Knowledge Management, CIKM 2002, pp. 438-445.
Crysandt, H. et al., “Music classification with MPEG-7,” Proceedings of the SPIE—The International Society for Optical Engineering, 2003, vol. 5021, pp. 397-404.
Shan, M.K. et al., “Music style mining and classification by melody,” IEICE Transactions of Information and Systems, Mar. 2003, vol. E86-D, No. 3, pp. 655-659.
Lu, J. et al., “Feature analysis for speech/music automatic classification,” Journal of Computer Aided Design & Computer Graphics, Mar. 2002, vol. 14, No. 3, pp. 233-237.
Hothker, K. et al., “Investigating the influence of representations and algorithms in music classification,” Computers and the Humanities, Feb. 2001, vol. 35, No. 1, pp. 65-79.
Pye, D., “Content-based methods for the management of digital music,” 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 4, pp. 2437-2440.
Tzanetakis, G., “Musical genre classification of audio signals,” IEEE Transactions on Speech and Audio Processing, Jul. 2002, vol. 10, No. 5, pp. 293-302.

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