Computational Music Theory and Analysis
Covers major approaches to analyzing musical scores using computers. Topics include AI/machine learning of style, musical similarity, encoding, music composition, music perception, and big data repertory studies. Programming assignments given in Python. Culminates in an original final project. Students taking graduate version complete different assignments. Enrollment limited.
Spring 2023
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 {{% resource_link "df43b258-7896-415a-a0f3-2ee18c498fa8" "Python" %}} using the MIT-created {{% resource_link "078ed66c-bf67-4c1b-adcd-caf5cc0c4c0e" "music21 toolkit." %}}
Readings
- Laptop computer with Windows or MacOS installed. If you choose to use Unix, etc., any problems with any assignments will fall entirely on you and I cannot provide technical support. Set up a dual-boot environment for win/ac.
Prerequisites & course information
Subject meets with 21M.583 Prereq: 6.1010 and ( 21M.301 or 21M.302 ) Acad Year 2027-2028: U (Spring) Acad Year 2028-2029: Not offered 3-0-9 units. HASS-A
Sources
- Course catalogue ↗2026–2027
- MIT OpenCourseWare ↗Spring 2023
- MIT OCW syllabus ↗
- Historical MIT OpenCourseWare offering. Named reading entries and schedule topics are automatically extracted from the linked source pages.