[Huron] = Huron, David. Sweet Anticipation: Music and the Pyschology of Expectation. MIT Press, 2006. ISBN: 9780262582780. [Preview with Google Books]
[Tymoczko] = Tymoczko, Dmitri. A Geometry of Music: Harmony and Counterpoint in the Extended Common Practice. Oxford University Press, 2011. ISBN: 9780199887507. [Preview with Google Books]
| SES # | TOPICS | DETAILS AND RESOURCES | READINGS AND LISTENING |
|---|---|---|---|
| 1 |
Introduction Overview and quantitative approaches to simple music theory Introduction to the study of music history as commonly practiced |
Lecture 1 slides (PDF - 4.3MB) |
Cook, Nicholas. “Computational and Comparative Musicology.” In Empirical Musicology: Aims, Methods, Prospects. Edited by Eric Clarke and Nicholas Cook. Oxford University Press, 2004. ISBN: 9780195167504. [Preview with Google Books] ———. “Towards a Complete Musicologist.” Proceedings of the International Society for Music Information Retrieval. Queen Mary, London, 2005. “Introduction.” Chapter 1 in [Huron]. ListeningLigeti, György. Lux Aeterna. Ensemble InterContemporain, Pierre Boulez. Deutsche Grammophon, 1988. Bartók, Bela. “String Quartet No. 4, Movement 1.” From Bela Bartók: The 6 String Quartets—Emerson Quartet. Deutsche Grammophon, 1990. |
| 2 | Introduction to computation and music I | Using Python to load music for analysis, encoding, transposing, and manipulating musical scores. Descriptive statistics. Introduction to the Eclipse IDE, Python, and music21. | |
| 3 |
Data analysis of repertories I Introduction to computation and music II |
Performing basic searches, finding significant musical features within a larger repertory. | |
| 4 |
Data analysis of repertories II Statistical significance in common-practice music (1750–1900) |
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| 5 | Musical representation for computers | Introduction to methods that have been used to encode music for analysis, playback, and notation; Craig Sapp’s “Rosetta Stone” for translating one notation format to another. | |
| 6 |
Assignment 2 presentations Computational methods in musicology: using music21 for music history research |
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| 7 |
Similarity and difference Searching repertories |
Uitdenbogerd, Alexandra, and Justin Zobel. “Matching Techniques for Large Music Databases.” Proceedings of ACM Multimedia 99. 1999, pp. 57–66. “Statistical Properties of Music.” Chapter 5 in [Huron]. pp. 73–89. |
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| 8 | Existing projects in quantitative and computational musicology I: rock corpora |
Clercq, Trevor de, and David Temperley. “A Corpus Analysis of Rock Harmony.” Popular Music 30, no. 1 (2011): 47–70. The CMME Project (computerized mensural music editing). |
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| 9 |
Markov chains Mathematical foundations of ancient Greek music |
Using geometry and algebra to study acoustics and scales, mathematical foundations of music notation. | Chapters 1–5 in [Tymoczko]. |
| 10 | Mathematical models of musical behavior I | Neoriemannian analysis, the work of David Lewin and other music theorists; Tymoczko’s Geometries. | |
| 11 | Mathematical models of musical behavior II | Development of the mathematical methods of the composer Elliot Carter (b.1908). | Harkleroad, Leon. “How Not to Mix Mathematics and Music.” Chapter 9 in The Math Behind the Music. Cambridge University Press, 2006, pp. 117–27. ISBN: 9780521009355. |
| 12 |
Midterm exam Final projects assigned and discussed |
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| 13 | Music perception: guest lecture by Dr. Peter Cariani | Cognitive and evolutionary foundations of music and experimental methods of testing them. Possible additional topics: advanced acoustics and waveform analysis, reading and misreading MRI data. | More on this topic: Dr. Cariani’s HST.725 Music Perception and Cognition. |
| 14 | Statistical methods for analyzing musical repertories | Bayesian mathematics of probability and expectation. |
Krumhansl, Carol. “A Key-finding Algorithm Based on Tonal Hierarchies.” In Cognitive Foundations of Musical Pitch. Oxford University Press, 1990, pp. 77–110. ISBN: 9780195054750. [Preview with Google Books] Cuthbert, Michael Scott. “Tipping the Iceberg: Missing Italian Polyphony from the Age of Schism.” Musica Disciplina 54 (2009): 39–74.
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| 15 | Computational methods in musicology: using music21 for music theory I | Roman numeral analysis of Bach chorales. K–L and Shannon divergence metrics; absolute distance vs. earth mover distances. | |
| 16 | Computational methods in musicology: using music21 for music theory II | Roman numeral analysis of Bach chorales: Algorithms for detecting key changes in Bach chorales (windowed analysis using probe-tones; single-pass average functionality scores; multiple pass sectional combining). | |
| 17 | Presentations on existing projects in digital musicology/music information retrieval II | Feature Extraction and Machine Learning. kNN, Tree-Building (overfitting), Majority. Cross-validation. |
Optional ReadingsCilibrasi, Rudi, Paul Vitányi, et al. “Algorithmic Clustering of Music Based on String Compression.” Computer Music Journal 28, no. 4 (2004): 49–67. McKay, Cory. Chapters on jSymbolic in Automatic Music Classification with jMIR. (PDF - 5.6MB) Cuthbert, Michael Scott, Christopher Ariza, and Lisa Friedland. “Feature Extraction and Machine Learning on Symbolic Music using the music21 Toolkit.” (PDF) Proceedings of the International Symposium on Music Information Retrieval. 2011, pp. 387–92. Cuthbert, Michael Scott, Christopher Ariza, et al. “Hidden Beyond MIDI’s Reach: Feature Extraction and Machine Learning with Rich Symbolic Formats in music21.” (PDF) Proceedings of the Neural Information Processing Systems Conference. Music and Machine Learning, Workshop 4, 2011. |
| 18 | Visualizing music, its structure, and its development over time | Tools for viewing musical structures and grasping large bodies of music. Effective data presentation and plotting musical form. |
Tufte, Edward. Chapters 1–2 in The Visual Display of Quantitative Information. Graphics Press, 2001. ISBN: 9780961392147. ———. The Cognitive Style of Powerpoint. Graphics Press, 2003. ISBN: 9780961392154. |
| 19 |
Leftovers: feature extraction and machine learning MITx: thoughts and designs |
music21 blog post on automated grading of common-practice music theory assignments: “music21 Theory Analyzer.” Feb 11, 2012. | |
| 20 | Musical form and reduction: guest lecture by Phillip Kirlin |
Computational reductive and Schenkerian analysis For this class, prepare a reduction of theme from Mozart’s Piano Sonata No. 11 in A major (K331). |
Kirlin, Phillip, and David Jensen. “Probabilistic Modeling of Hierarchical Music Analysis.” (PDF) In Proceeding of the International Symposium in Music Information Retrieval. 2011. |
| 21 | Expectation, anticipation, and music cognition in rhythm |
Chapters 3–4 and especially Chapter 10 in [Huron]. Optional Reading
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| 22 | Non-western music and digital humanities: guest lecture by Joren Six | Six, Joren, and Olmo Cornelis. “Tarsos—A Platform to Explore Pitch Scales in Non-Western and Western Music.” (PDF) Proceedings of the International Society for Music Information Retrieval. 2011, pp. 169–74. | |
| 23 | Xenakis sieve applications using music21 |
Ariza, Christopher, and Michael Scott Cuthbert. “Analytical and Compositional Applications of a Network-Based Scale Model in music21.” (PDF - 5.5MB) Proceedings of the International Computer Music Conference. 2011, pp. 701–8. Ariza, Christopher. “Designing and Deploying Non-Octave-Repeating Scales with the Xenakis Sieve.” In Exploring Xenakis. Edited by Sharon Kanach. Pendragon Press, 2012 forthcoming. ListeningXenakis, Iannis. “Nomos Alpha.” 20th Century: Xenakis—Atrees / Nomos Alpha. Pierre Penassou, cello. EMI Classics, 2010 reissue. ———. “À R (Hommage à Maurice Ravel).” Xenakis: Works for Piano, Vol. 4. Aki Takahashi, piano. Mode, 1999. |
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| 24 | Grab Bag: Peachnote; isolating flaws in computational music studies; first student presentation | Viro, Vladimir. “Peachnote: Music Score Search and Analysis Platform.” (PDF) Proceedings of the International Computer Music Conference. 2012, pp. 359–62. | |
| 25 | Student presentations | ||
| 26 | Student presentations (cont.) |
