Compilation of References - Intelligent Music Information Systems: Tools and Methodologies - page 352

Tzanetakis, G. (2002). Manipulation, analysis and
retrieval systems for audio signals . Unpublished doc-
toral thesis, Computer Science Department, Princeton
University.

Unal, E., Narayanan, S. S. & Chew, E. (2004). A statistical
approach to retrieval under user-dependent uncertainty
in query-by-humming systems. In Proceedings of the
6th ACM SIGMM International Workshop on Multime-
dia Information Retrieval (MIR'04) (pp. 113-118) New
York, New York.

Tzanetakis, G., & Cook, P. (1999). A framework for audio
analysis based on classification and temporal segmen-
tation. In EUROMICRO 1999 (25th EUROMICRO '99
Conference, Informatics: Theory and Practice for the
New Millenium) .

Unal, E., Narayanan, S. S., Shih, H. H., Chew, E. &
Kuo, C. C. (2003). Creating data resources for designing
user-centric frontends for query by humming systems.
In Proceedings of the 5th ACM SIGMM International
Workshop on Multimedia Information Retrieval (MIR'03)
(pp. 116-121) Berkeley, California.

Tzanetakis, G., & Cook, P. (2002). Musical genre clas-
siication of audio signals. IEEE Transactions on Speech
and Audio Processing , 10 (5), 293-302.

University of Waikato (n.d.). New Zealand digital library.
Retrieved May 21, 2007, from http://www.nzdl.org

Tzanetakis, G., Ermolinskyi, A., & Cook, P. (2002).
Beyond the query-by-example paradigm: New query in-
terfaces for music information retrieval. In Proceedings of
the 2002 International Computer Music Conference .

Van Baelen, E., & De Raedt, L. (1996). Analysis and
prediction of piano performances using inductive logic
programming. International Conference in Inductive
Logic Programming , 55-71.

Tzanetakis, G., Essl, G., & Cook, P. (2001). Automatic
musical genre classification of audio signals. In Proceed-
ings of the International Symposium on Music Informa-
tion Retrieval (pp. 205-210).

van Rijsbergen, C. J., (1979). Information retrieval (2 nd
ed.). London: Butterworths.

Tzanetakis, G., Gao, J., & Steenkiste, P. (2003). A scalable
peer-to-peer system for music content and information
retrieval. In Proceedings of the International Conference
on Music Information Retrieval .

Vapnik, V. N. (1995). The nature of statistical learning
theory . New York: Springer.

Vignoli, F. (2004). Digital music interaction concepts:
A user study. In Proceedings of the Intern. Conference
on Music Information Retrieval . Tzanetakis, G., Gao, J., & Steenkiste, P. (2004). A scal-

able peer-to-peer system for music information retrieval.
Computer Music Journal , 28 (2), 24-33.

W3C Document, Extensible Markup Language 1.0.
(1988), second edition (2000), http://www.w3.org/TR/
REC-xml/, Third Edition, W3C Recommendation 04
February 2004, see http://www.w3.org/TR/2004/REC-
xml-20040204/

Uitdenbogerd, A., & Zobel, J. (1998). Manipulation of
music for melody matching. In Proceedings of the 6th
ACM Conference on Multimedia (MM'98) (pp. 235-240).
Bristol, UK.

Wadge, W. and Ashcroft, E. (1985). Lucid, the dataflow
programming language. APIC Studies in Data Process-
ing . New York: Academic Press.

Uitdenbogerd, A., & Zobel, J. (1999). Melodic matching
techniques for large music databases. In Proceedings of
the 7th ACM Conference on Multimedia (MM'99) (pp.
57-66) Orlando, Florida.

Waldman, M., Cranor, L., & Rubin, A. (2002). Trust.
In A. Oram (Ed.), Harnessing the power of disruptive
technologies (pp. 242-270). Sebastopol: O'Reilly. Ukkonen, E. (1992). Approximate string-matching with

q-grams and maximal matches. Theoretical Computer
Science , 92 , 191-211.

Wang, A. (2003). An industrial strength audio search
algorithm. In Proceedings of the 4th International