Compilation of References - Intelligent Music Information Systems: Tools and Methodologies - page 343

verification and deduction in many-valued knowledge
systems. Soft Computing, 3 (1), 7-19.

findings. In Proceedings of the International Conference
on Music Information Retrieval .

Laita, L. M., Roanes-Lozano, E., Maojo, V., de Ledesma,
L., & Laita, L. (2000). An expert system for managing
medical appropriateness criteria based on computer
algebra techniques. Computers and Mathematics with
Applications, 51 (5), 473-481.

Lerdahl, F., & Jackendoff, R. (1983). A generative theory
of tonal music . Cambridge, MA: MIT Press.

Lerdhal, F. & Jackendoff, R. (1983). A generative theory of
tonal music . Cambridge: The MIT Press.

Lesaffre, M., Leman, M., Tanghe, K., De Baets, B., De
Meyer, H. & Martens, J.-P. (2003). User-dependent tax-
onomy of musical features as a conceptual framework for
musical audio-mining technology. In Proceedings of the
Stockholm Music Acoustics Conference (pp. 635-638).

Lank, E. H. (2003). A retargetable framework for in-
teractive diagram recognition. In Proceedings of the
International Conference on Document Analysis and
Recognition (ICDAR'03) (pp. 185-189).

Large, E. W., & Kolen, J. F. (1994). Resonance and
the perception of musical meter. Connection Science,
6 (2/3), 177-208.

Lesavich, S. (2002). Protecting digital content in a peer-
to-peer world. Retrieved May 27, 2007, from http://www.
hightech-iplaw.com/wibardrm.pdf

Lartillot, O. (2005). Efficient extraction of closed motivic
patterns in multi-dimensional symbolic representations
of music. In Proceedings of the International Conference
on Music Information Retrieval (pp. 191-198).

Lethin, R. (2001). Reputation. In A. Oram (Ed.), Harness-
ing the power of disruptive technologies (pp. 341-353).
Sebastopol: O'Reilly.

Levitin, D. J., & Cook, P. R. (1996). Memory for musical
tempo: Additional evidence that auditory memory is ab-
solute. Perception and Psychophysics , 58 (6), 927-935.

Lartillot, O., & Toiviainen, P. (2007). Motivic matching
strategies for automated pattern extraction. Musicae
Scientiae , Discussion Forum 4A, 281-314.

Levitin, D. J. (1994). Absolute memory for musical pitch:
Evidence from the production of learned melodies. Per-
ception and Psychophysics , 56 (4), 414-423.

Lavrenko, V. & Pickens, J. (2003). Polyphonic music
modeling with random fields. In Proceedings of the
ACM International Conference on Multimedia (pp.
120-129).

Lew, M. S., Sebe, N., Djeraba, C., & Jain, R. (2006).
Content-based multimedia information retrieval: State
of the art and challenges. ACM Transactions on Multi-
media Computing, Communications and Applications,
2 (1), 1-19.

Lee, H., Jang, J. R., & Kao, M. (2001). Content-based
music retrieval using linear scaling and branch-and-
bound tree search. In Proceedings of IEEE International
Conference on Multimedia and Expo .

Li, G. (1985). Robust regression. In D. C. Hoaglin, F.
Mosteller & J. W. Tukey (Eds.), Exploring data tables,
trends, and shapes (pp. 281-343). New York: John Wiley
and Sons.

Lee, J. H. & Downie, J. S. (2004). Survey of music infor-
mation needs, uses, and seeking behaviours: Preliminary
findings. In Proceedings of the International Conference
on Music Information Retrieval (pp. 441-446).

Li, T. & Tzanetakis, G. (2003). Factors in automatic
musical genre classification of audio signals. In Proceed-
ings of the IEEE Workshop on Applications of Signal
Processing to Audio and Acoustics (WASPAA) New
Paltz, New York.

Lee, J. H. (1997). Analysis of multiple evidence combina-
tion. In Proceedings of the ACM International Conference
on Research and Development in Information Retrieval
(pp. 267-275).

Lee, J. H., & Downie, J. S. (2004). Survey of music infor-
mation needs, uses, and seeking behaviours: Preliminary