Syllabus

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

Buy at MIT Press 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.

Grading Scale

Many assignments and questions on exams will be graded 0–5 (some assignments will be weighed more than others, however). The scale will be very different than what you might be used to. Expect to get scores in the 2–4 range. 60% of the typical grading scale is wasted on failing grades, while only 20% of the scale (80%–100%) decides between adequate and exceptional answers. The expectation here is that most answers will be good or better and that we don’t need to waste the scale on work that does not meet this expectation:

QUALITY OF WORK TYPICAL SCALE THIS SCALE APPROXIMATE GRADE
Exceptional (see below) 5 (100%) 5 (100%) A+
Superb 4.8 (96%) 4.5+ (90%) A
Excellent 4.6 (92%) 4+ (80%) A–
Very Good 4.4 (88%) 3+ (60%) B+
Good 4.25 (85%) 2.5+ (50%) B
Acceptable to Adequate 4 (80%) 2+ (40%) B–
Barely Adequate 3.5 (70%) 1.5+ (30%) C
Substantially Flawed 3 or lower (60%) 1 or lower (20%) D (1+) or F (0–1)

A consequence of this scale is that, for instance, two excellent assignments (4pts each) and one missing (0) will average 2.7 points (B/B+), while three adequate assignments (2pts each) will earn a B–/C+. The traditional system would give the first student a D+ and makes it nearly impossible to get an A or B+ range grade if you have even one bad or missing assignment (this is why so many professors fudge grades). There is also little reward in this scale for trying to fill space on an exam on an answer you know absolutely nothing about, since the difference between a D and a 0 is little. Of course, if you know just a little, you should write what you do know.

The final consequence of the scale is it lets me reserve the grade of ‘5’ for truly mind-blowing work. A 4.5 is usually the maximum given for a “perfect” assignment, while a 5 is a once or twice a year event. At the end of the semester, the important cutoff between A– and B+ is usually between 3.5 and 4.

(Note that this weird scale doesn’t mean that the grades will be higher or lower than typical MIT classes—that is not my intent. I just hope that they are more fair in the end).

Course Policies

These are bureaucratic things that should just be assumed but occasionally are not (i.e. the “terms of service”):

  1. The only excused absences from classes or assignments are those which are requested and approved ahead of time (do not ask the night before if it’s a conflict that you’ve known about for a long time).
  2. Late assignments will be accepted at the instructor’s discretion only and generally penalized (usually 1/3–1 letter grade per class). Missed quizzes will not be made up without prior permission (not just notice) or a doctor’s note.
  3. If you have a medical condition that affects your performance in class, please tell me now and present a note from the Dean testifying to the condition by the first class of the second week.
  4. If you miss class (for any reason), it is first your responsibility to get the assignments from someone else in class (as soon as possible) and to go over his or her notes about the lecture; only after you have done this should you ask me to discuss the lecture with you.
  5. Ultimate discretion in grading, rules, etc. is reserved for the instructor.

Course Info

As Taught In
Spring 2012
Learning Resource Types
Lecture Notes
Projects with Examples
Written Assignments with Examples
Problem Sets