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endnotes

1

This research is suppor ted by the
ΗΡΑΚΛΕΙΤΟΣ and ΠΥΘΑΓΟΡΑΣ ΙΙ na-
tional programs funded by the ΕΠΕΑΕΚ.

Typke, R., Wiering, F., Veltkamp, R. C. (2005). A
survey of music information retrieval systems. In
Proceedings International Conference on Music
Information Retrieval (ISMIR) (pp. 153-160).

2

Each element in an acoustic sequence is in
the range 0-255, thus it requires one byte.

3

In the case of ML, R could consist of all
the n DWT coefficients. However, the n
sequence elements in the time domain are
selected just to avoid the computation of
the inverse DWT, since the time domain
presents a smaller storage requirement as
the data values are in range 0-255.

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

Tzanetakis, G., Gao, J., & Steenkiste, P. (2004). A
scalable peer-to-peer system for music informa-
tion retrieval. Computer Music Journal , 28 (2),
24-33.