Week 2 - Analysis of symbolic representations

Notes:

• MIR with Symbolic Representations

Ipython notebooks:

• Symbolic MIR with Music21
• Audio and Image I/O

Homework

due: Friday January 24th

Perform an analysis of a set of MIDI files of your choice. Set out a hypo thesis or goal for analysis, then perform it and discuss the results.

Submit an ipython notebook or a similar document from another environment showing the work performed, including a description of the source data set and the discussion of the results.

Reading:

Cuthbert, M., Ariza, C., & Friedland, L. (2011). Feature Extraction and Machine Learning on Symbolic Music using the music21 Toolkit. In ISMIR. Retrieved from http://web.mit.edu/music21/papers/Cuthbert_Ariza_Friedland_Feature-Extraction_ISMIR_2011.pdf

Additional Readings:

McKay, C., & Fujinaga, I. (2006). jSymbolic: A feature extractor for MIDI files. In Proceedings of the International Computer Music Conference 2006. Retrieved from https://www.music.mcgill.ca/~cmckay/papers/musictech/McKay_ICMC_06_jSymbolic.pdf

Mckay, C. (2004). Automatic Genre Classification of MIDI Recordings. PhD Thesis.

Knopke, I. (2012). Chapter 11 : Symbolic Data Mining in Musicology. In Music Data Mining (pp. 327–345).