Course Meeting Times
Alternating lecture/discussions and labs: 2 sessions / week, 1.5 hours / session
Overview
The disciplines of music history and music theory have been slow to embrace the digital revolutions that have transformed other fields’ text-based scholarship (history and literature in particular). Music history has tended to focus on large trends based on subjective (but expert) feelings from having heard and digested a large quantity of music, but the repertories are not always representative of the full range of material being produced. Music theory on the other hand has progressed through careful study of singular pieces of music, also often unrepresentative. Computational musicology opens the door to the possibility of understanding—even if at a broad level—trends and norms of behavior of large repertories of music. But the challenges of computational and quantitative approaches are many: from difficulty in getting repertories encoded for computers to read, to problems in making tools to understand music, to data analysis and drawing conclusions, to convincing non-technological skeptics about the importance and accuracy of these conclusions especially when they contradict existing ideas.
This course presents the major approaches, results, and challenges of computational musicology through readings in the field, gaining familiarity with datasets, and hands-on workshops and assignments on data analysis and “corpus” (i.e., repertory) studies. Approximately every other class session will be a discussion/lecture, with alternating classes being labs in using digital tools for studying music. The class culminates in an independent research project in quantitative or computational musicology that will be presented to the class as a whole.
Prerequisites
A background in music theory and/or history is required.
Prior experience in computer programming (for example, 6.01 Introduction to Electrical Engineering and Computer Science I) will be extremely helpful.
Requirements and Grading
Since the class is small, we will be able to hear all of each other’s thoughts and ideas nearly every class meeting. Thus, participation, preparation, and presentations will form the biggest graded components of the subject. Don’t worry if you’re shy! All of us will do our best to make the discussion as open and welcoming as possible.
The approximate grade break-down will be:
| ACTIVITIES | PERCENTAGES |
|---|---|
| Midterm exam | 10% |
| Final paper | 20% |
| Final oral presentation | 10% |
| Other assignments | 30% |
| Attendance, preparation, and participation | 30% |
A failing grade may be assigned for failure of any of the components of the class.
For this class you will answer three “problem-set” style assignments, produce one short paper (3–5pp.), a final paper (10–12pp.) and 10-minute presentation on a research project of your choice, and 4–5 short “problem set” like worksheets, one-page responses, etc. designed to give you an opportunity to practice and reinforce concepts and skills important to the success of your final research project.
Actually, one final requirement—it’s great music and great new approaches, so let’s enjoy it. Please let me know if you ever have concerns about the class or if you have suggestions for changes or improvements.
More details on grading scale and course policies.
Required Texts
Huron, David. Sweet Anticipation: Music and the Pyschology of Expectation. MIT Press, 2006. ISBN: 9780262582780. [Preview with Google Books]
Tymoczko, Dmitri. A Geometry of Music: Harmony and Counterpoint in the Extended Common Practice. Oxford University Press, 2011. ISBN: 9780199887507. [Preview with Google Books]
Calendar
| SES # | TOPICS | KEY DATES |
|---|---|---|
| 1 |
Introduction Overview and quantitative approaches to simple music theory Introduction to the study of music history as commonly practiced |
Install Eclipse and music21 Assignment 1 out |
| 2 | Introduction to computation and music I |
Assignment 1 due Assignment 2 out |
| 3 |
Data analysis of repertories I Introduction to computation and music II |
|
| 4 |
Data analysis of repertories II Statistical significance in common-practice music (1750–1900) |
|
| 5 | Musical representation for computers | Assignment 2 due |
| 6 |
Assignment 2 presentations Computational methods in musicology: using music21 for music history research |
Assignment 3 out |
| 7 |
Similarity and difference Searching repertories |
|
| 8 | Existing projects in quantitative and computational musicology: rock corpora | Assignment 4 out |
| 9 |
Markov chains Mathematical foundations of ancient Greek music |
Assignment 3 due |
| 10 | Mathematical models of musical behavior I | |
| 11 | Mathematical models of musical behavior II: Elliot Carter | |
| 12 |
Midterm exam Final projects assigned and discussed |
|
| 13 | Music perception: guest lecture by Dr. Peter Cariani | Assignment 4 due |
| 14 | Statistical methods for analyzing musical repertories I | |
| 15 | Computational methods in musicology: using music21 for music theory I | |
| 16 | Computational methods in musicology: using music21 for music theory II | |
| 17 | Presentations on existing projects in digital musicology/music information retrieval | |
| 18 | Visualizing music, its structure, and its development over time | Final paper descriptions due |
| 19 |
Leftovers: feature extraction and machine learning MITx: thoughts and designs |
|
| 20 | Musical form and reduction: guest lecture by Phillip Kirlin | |
| 21 | Expectation, anticipation, and music cognition in rhythm | |
| 22 | Non-western music and digital humanities: guest lecture by Joren Six | |
| 23 | Xenakis Sieve Applications using music21 | |
| 24 | Grab Bag: Peachnote; isolating flaws in computational music studies; first student presentation | Final paper due |
| 25 | Student presentations | |
| 26 | Student presentations (cont.) |
Topics not covered in this version of the class: history and development of music information retrieval; Monte Carlo methods.
