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mensional representations of polyphonic music. Journal
of New Music Research, 31 (4), 321-345.
Miyao, H., & Maruyama, M. (2004). An online hand-
written music score recognition system. In Proceedings
of the Seventeenth International Conference on Pattern
Recognition (ICPR'04) (pp. 461-464).
Middleton, R. (2002). Studying popular music . Phila-
delphia: Open University Press.
Moen, W. E. (1998). Accessing distributed cultural
heritage information. Communications of the ACM ,
41 (4), 45-48.
MIDI (1993). MIDI 1.0 detailed specification, version 4.3 .
Los Angeles, CA: International MIDI Association.
Mierswa, I. (2004). Automatisierte Merkmalsextraktion
aus Audiodaten. Fachbereich Informatik, Universität
Dortmund, 2004.
Mongeau, M. & Sankoff, D. (1990). Comparison of
musical sequences. Computers and the Humanities ,
24 , 161-175.
Mierswa, I., & Morik, K. (2005). Automatic feature
extraction for classifying audio data. Machine Learning
Journal , 58 , 127-149.
Moore, B. C., & Glasberg, B. (1983). Suggested formulae
for calculating auditory-filter bandwidths and excita-
tion patterns. The Journal of the Acoustical Society of
America, 74 (3), 750-753.
Mierswa, I., & Wurst, M. (2005). Efficient feature con-
struction by meta learning: Guiding the search in meta
hypothesis space. In Proceedings of the International
Conference on Machine Learning Workshop on Meta
Learning .
Mörchen, F., Ultsch, A., Nöcker, M., & Stamm, C. (2005).
Databionic visualization of music collections according to
perceptual distance. In Proceedings of the International
Conference on Music Information Retrieval .
Milojicic, D., et al. (2002). Peer-to-peer computing, HP
laboratories. Retrieved May 27, 2007, from http://www.
hpl.hp.com/techreports/2002/HPL-2002-57.pdf
Mörchen, F., Ultsch, A., Thies, M., Löhken, I., Nöcker,
M., Stamm, C., Efthymiou, N., & Kümmerer, M. (2004).
MusicMiner: Visualizing perceptual distances of music as
topograpical maps (Tech. Rep.). Dept. of Mathematics and
Computer Science, University of Marburg, Germany.
Minar, N., & Hedlung, M. (2001). Network of peers:
Peer-to-peer models through the history of the internet.
In A. Oram (Ed.), Harnessing the power of disruptive
technologies (pp. 3-20). Sebastopol: O'Reilly.
Morik, K., & Wessel, S. (1999). Incremental signal to
symbol processing. In K. Morik, M. Kaiser, & V. Kling-
spor (Eds.), Making robots smarter: Combining sensing
and action through robot learning (pp. 185-198). Boston:
Kluwer Academic.
Mirapaul, M. (2004, June 17). Art unfolds in a search
for keywords. The New York Times, CLIII(52883) , E5.
New York.
Mouchère, H., & Anquetil, E. (2006). A unify strategy
to deal with different natures of reject. In Proceedings
of the Eighteenth International Conference on Pattern
Recognition (ICPR'06) (pp. 792-795) .
Misra, A., Wang, G. & Cook, P. (2005). SndTools: Real-
time audio DSP and 3D visualization. In Proceedings of
the International Computer Music Conference (ICMC) ,
Barcelona, Spain.
Mitchell, T. M. (1997). Machine learning. McGraw-
Hill.
Mulholland, J. (2001). Digital rights (mis)management,
W3C DRM workshop. Retrieved May 27, 2007, from
http://www.w3.org/2000/12/drm-ws/pp/fsu-mulholland.
html
Mitobe, Y., Miyao, H., & Maruyama, M. (2004). A
fast HMM algorithm based on stroke lengths for online
recognition of handwritten music scores. In Proceedings
of the Ninth International Workshop on Frontiers in
Handwriting Recognition (IWFHR'04) (pp. 521-526).
Muller, M., Kurth, F., & Roder, T. (2004). Towards an
efficient algorithm for automatic score-to-audio syn-
chronization. In Proceedings of the 5th International
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