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21M.383 | Spring 2023 | Undergraduate, Graduate

# Computational Music Theory and Analysis

#### Course Description

This course presents major approaches to computational music theory and musicology in the symbolic (score-based) domain. It covers algorithms for music theory, encoding, corpus studies, musical search and similarity, feature extraction and machine learning, music generation, and computational music perception. Other …
This course presents major approaches to computational music theory and musicology in the symbolic (score-based) domain. It covers algorithms for music theory, encoding, corpus studies, musical search and similarity, feature extraction and machine learning, music generation, and computational music perception. Other topics include repertory access and computational bias. Programming assignments are in Python using the MIT-created music21 toolkit.

## Course Info

##### Instructor

- Prof. Michael Scott Asato Cuthbert

##### Departments

- Music and Theater Arts

##### Topics

- Fine Arts
  - Music
    - Music History
    - Music Theory
- Mathematics
  - Computation
  - Mathematical Analysis
  - Probability and Statistics

##### Learning Resource Types

notes Lecture Notes
theaters Lecture Videos
theaters Other Video
assignment Multiple Assignment Types
grading Exams
Editable Files
Visualization created in music21 based on the Krumhansl-Schmuckler key-finding algorithm overlaid with a few measures of the MusicXML score it represents, Giuseppe Verdi’s “La donna è mobile” aria. (Source: Music21 User Guide. License: music21 is free and open-source software, licensed under the BSD License; Verdi score is in the public domain.)

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