[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 |
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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) |
———. "Towards a Complete Musicologist." Proceedings of the International Society for Music Information Retrieval. Queen Mary, London, 2005. "Introduction." Chapter 1 in [Huron]. Listening
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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). |
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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. |
![]() 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 Cuthbert, Michael Scott, Christopher Ariza, and Lisa Friedland. "Feature Extraction and Machine Learning on Symbolic Music using the music21 Toolkit." ( Cuthbert, Michael Scott, Christopher Ariza, et al. |
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. |
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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. ![]() |
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. ![]() |
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23 | Xenakis sieve applications using music21 |
Ariza, Christopher, and Michael Scott Cuthbert. Ariza, Christopher. "Designing and Deploying Non-Octave-Repeating Scales with the Xenakis Sieve." In Exploring Xenakis. Edited by Sharon Kanach. Pendragon Press, 2012 forthcoming. Listening
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24 | Grab Bag: Peachnote; isolating flaws in computational music studies; first student presentation | Viro, Vladimir. ![]() |
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25 | Student presentations | ||
26 |
Student presentations (cont.) |