Computational music analysis 3106-SAM-F
The course introduces the foundational concepts and methods used in
computational music analysis. It covers the history of the field, its scope,
topics, and major research projects. Technical aspects are also
addressed, including programming languages commonly used in
musicological analysis (bash, Perl, Python, C++) and their practical
applications. A significant component of the course involves
methodologies for acquiring data and creating digital editions and corpora
for research purposes. Students will gain experience with tools for
analyzing repertories encoded in the Humdrum format (Humdrum Toolkit,
Humdrum Extras, humlib). They will also learn to use the UNIX terminal
(Linux, macOS, or Windows Subsystem for Linux [WSL]), basic bash
commands, and how to create simple scripts with loops and conditional
statements. Special attention will be paid to the practical application of
Humdrum analytical tools, their outputs, and data flow management.
Students are required to bring their own laptops with Windows 10 v.1903
or higher, Linux (Mint v.21 or higher, Ubuntu 18.04 or higher), or macOS.
If this is not possible, students should contact the student affairs office for
assistance. Internet access is required, and eduroam certificate
configuration is recommended.
Prerequisite: Completion of the course "Digital Music Editions."
Type of course
Mode
Course coordinators
Learning outcomes
Knowledge
The student knows the history of computational music research.
The student understands the basics of computer operations and
operating systems.
The student knows what programming languages are, how they
work, and what they are used for.
The student understands how data streams, regular
expressions, and logical loops function.
The student is familiar with Humdrum analytical tools and the
structure of their output data.
The student understands how to acquire and create digital
edition corpora for musicological analysis.
Skills
The student can apply data streams, regular expressions, and
logical loops in practice.
The student can write and execute bash scripts.
The student can use Humdrum tools for musicological analysis
in a UNIX terminal environment.
Assessment criteria
Attendance: Two absences are allowed (excused or unexcused).
Any further absences, up to a maximum of three per semester,
must be made up. In the case of extended absences, students
must contact the course instructor.
Final grade: Based on a submitted term project.
Bibliography
Huron, David, and Craig Stuart Sapp. “Humdrum: The Humdrum
Toolkit for Computational Music Analysis.” Accessed April 25,
2025. https://www.humdrum.org
Konik, Marcin. “Repertuar muzyki polskiej wieku XIX – wydania
cyfrowe i analiza skomputeryzowana.” Studia Chopinowskie 1,
no. 1 (2024): 54–81. https://doi.org/10.56693/sc.92
Konik, Marcin, Craig Stuart Sapp, and Jacek Iwaszko. “Polish
Music Heritage in Open Access.” Journal of New Music
Research, 2025.
https://doi.org/10.1080/09298215.2025.2487093
Poudrier, Ève, and Craig Stuart Sapp. “Polyrhythm Analysis
Using the Composite Tool.” In Proceedings of the 9th
International Conference on Digital Libraries for Musicology
(DLfM), 65–73. New York, 2022.
https://doi.org/10.1145/3543882.3543890
Ricciardi, Emiliano, and Craig Stuart Sapp. “Editing Madrigals in
the Digital Age: The Tasso in Music Project.” In Music Encoding
Conference Proceedings, edited by Elsa De Luca and Julia
Flanders, 25–40. https://doi.org/10.17613/17a5-2b65 Sapp, Craig Stuart. Verovio Humdrum Viewer Documentation.
2025. https://doc.verovio.humdrum.org
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: