Embedded Files
features
features
levels
low
middle
high
meta
features extraction
features imitation
features manipulation
demonstration (2020)
O. Cornelis, M. Lesaffre, D. Moelants, and M. Leman, “Access to Ethnic Music: Advances and Perspectives in Content-based Music Information Retrieval,” Signal Processing, vol. 90, no. 4, pp. 1008–1031, 2010. p. 1011.
N. Orio, “Music Retrieval: A Tutorial and Review,” Foundations and Trends in Information Retrieval, vol. 1, no. 1, pp. 1–90, 2006. p. 14.
Z. Fu, G. Lu, K. M. Ting, and D. Zhang, “A Survey of Audio-Based Music Classification and Annotation,” IEEE Transactions on Multimedia, vol. 13, no. 2, pp. 303–319, 2011. p. 304.
Z. Fu, G. Lu, K. M. Ting, and D. Zhang, “A Survey of Audio-Based Music Classification and Annotation,” IEEE Transactions on Multimedia, vol. 13, no. 2, pp. 303–319, 2011. p. 305.
higher-level
higher-level
M. A. Casey, R. Veltkamp, M. Goto, M. Leman, C. Rhodes, and M. Slaney, “Content-Based Music Information Retrieval: Current Directions and Future Challenges,” Proceedings of the IEEE, vol. 96, no. 4, pp. 668–696, 2008. p. 672.
P. E. Savage, E. Merritt, T. Rzeszutek, and S. Brown, “CantoCore: A New Cross-cultural Song Classification Scheme,” Analytical Approaches World Music, vol. 2, no. 1, pp. 87–137, 2012. p. 4.
P. E. Savage, S. Brown, E. Sakai, and T. E. Currie, “Statistical Universals Reveal the Structures and Functions of Human Music,” PNAS, vol. 112, no. 29, pp. 8987–8992, 2015. p. 8990.
MIDI features
MIDI features
C. McKay, “Automatic Genre Classification of MIDI Recordings,” master’s thesis, McGill University, 2004. p. 107.
features based on
pp. 63–65: instrumentation
pp. 65–67: musical texture
pp. 67–72: rhythm
pp. 72: dynamics
pp. 72–74: pitch statistics
pp. 75–76: melody
pp. 76–78: chords
Most of them are included in the music21 Python library.
assignment 4
assignment 4
Automatic Genre Classification of MIDI Recordings (2004)
Read the paper and try to understand those features.
We will discuss in week 08.
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