Automatic Mood Detection from Electronic Music Data

12 November 2004
Abstract
Automatic mood detection from music has two main benefits. Firstly, having the knowledge of mood in advance can allow for possible enhancement of the music experience (such as mood-based visualizations) and secondly it makes 'query by mood' from music data-banks possible. This research is concerned with the automatic detection of mood from the electronic music genre, in particular that of drum and bass. The methodology was relatively simple, firstly sampling the music, and then giving a human pre-classification to the music (to use for training a classifier) via a point on a Thayer's model mood map. The samples then had low level signal processing features, mel frequency cepstral coefficient, psychoacoustic features and pitch image summary features extracted from them. These were then verified as useful via self organising maps and ranking via the feature selection techniques of information gain, gain ratio and symmetric uncertainty. The verified features were then used as training and testing (via cross-validation) data for a 3 layer perceptron neural network.
Out of this research comes understanding of how one human's approximated perception can be captured and shows its use for determination of mood classifications from music.
Automatic Mood Detection from Electronic Music Data
Lincoln
A dissertation submitΒιβλιοθήκη Baidued for the partial fulfilment of the requirements for the degree of Bachelor of Commerce (Honours) at the University of Otago, Dunedin, New Zealand
Acknowledgmts
The author would like to express his thanks to the following parties their help and support pertaining to the successful completion of this paper.
[13] [14]
Kohonen, T. "Self-Organising Maps. Second Edition", Springer, 50 p, 2001. Krumhansl, C. L. Cognitive Foundations of Musical Pitch, Oxford Psychology Series 17, Oxford University Press, New York and Oxford, 1990.
Dr. Da Deng — Supervisor
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Information Science fourth year class of 2004
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Refrncs
[1] Ahrendt, P., Meng, A., Larsen, J. "Decision time horizon for music genre classification using short time features". Submitted for EUSIPCO, 2004. [2] Bishop, C. M. "Neural Networks for Pattern Recognition". Oxford University Press. http://www.ncrq.aston.ac.uk/NNPR/ , 1995. Cheng, K., Nazer, B., Uppuluri, J., Verret, R. "Beat This: A Beat Synchronization Project'', Ow'net Group, Rice University, (Retrieved on 7 May 2004 from http://www.owlnet.rice.edu/-elec301/Proiects01/beat sync/index.html ), 2003. [4] Dahlhaus, C., Gjerdingen, C., Robert 0., "Studies in the Origin of Harmonic Tonality", Princeton University Press, ISBN 0691091358, 1990. Demuth H. and Beale M. "Neural Network Toolbox for use with Matlab Documentation", MathWorks, http://www.mathworks.com , 1998. [6] Deva, B. C. "Psychoacoustics of Music and Speech". I. M. H. Press Ltd, 1967. Golub, S. "Classifying recorded music". Unpublished masters thesis. University of Edinburgh. (Retrieved May 28 2004 from http://www.aiqeek.com/aimsc/ ), 2000. [8] Grimaldi, M., Cunningham, P., Kokaram, A. "An Evaluation of Alternative Feature Selection Strategies and Ensemble Techniques for Classifying Music", to appear in Workshop in Multimedia Discovery and Mining, ECML/PKDD03, Dubrovnik, Croatia, September, 2003. Haykin, S. "Neural networks: a comprehensive foundation". Upper Saddle River, N.J. , Prentice Hall, 1999. [10] Healey, J., Paccar, R., and Dabek, F. new affect-perceiving inter-face and its
Two approaches at feature extraction were used due to the first approach performing poorly at self organising map based cluster analysis. The mood classification scheme was then simplified to have four moods as opposed to 25. The main difference, however between the two approaches was based around different feature extraction window duration and different features. The second approach's features were used to train the neural network and the classification performed with classification accuracy rates no less than 84 %.
[15] [16]
Larsen, J. "Introduction to Artificial Neural Networks" IMM, 1999. Leman, M., Lesaffre, M., Tanghe, K. "An introduction to the IPEM Toolbox for Perception Based Music Analysis", Mikropolyphonie - The Online Contemporary Music Journal, Volume 7, 2001.
[3 ]
[5]
[71
[91
application to personalized music selection". Technical Report 478, Massachusetts Institute of Technology, Media Laboratory Perceptual 65 Computing Section. http://www-white.media.mit.edu/tech-reports/TR-478/TR-478.html , 1998.
[11]
Huron, D. and Aarden, B. "Cognitive Issues and Approaches in Music Information Retrieval". edited by S. Downie and D. Byrd (Retrieved on 7 May 2004 from htt p ://www.music-cod.ohio-state.edu/Huron/Publications/huron.MIR.conference.html ) , 2002.
[12]
Juslin, P.N. "Cue Utilization in communication of emotion in music performance: Relating performance to perception", Experimental Psychology, 26, pp. 1797-1813, 2000.