Digital musicology

Summary

This course will introduce students to the central topics in digital musicology and core theoretical approaches and methods. In the practical part, students will carry out a number of exercises.

Content

Keywords

music, digital humanities, musicology, music theory, data science, music cognition

Learning Prerequisites

Required courses

Required course (obligatory):

• Foundations of algebra, statistics and data analysis
• Basic programming (e.g. Python, Julia)

Recommended courses

Recommended background:

• CS320 Computer Language Processing (BA5)

• CS251 Theory of Computation (BA4)

Important concepts to start the course

Prior knowledge of music theory (e.g. notation, scales, chords) is desirable and beneficial, but the class can be completed without it.

Students with little or no experience in score reading may consult introductory texts such as:

• Henry, E., Snodgrass, J., and Piagentini, S. (2019). Fundamentals of music: Rudiments, musicianship, and composition, 7th ed., Pearson.

• Taylor, E.  R. (1999). The AB guide to music theory. 5 vols. Associated Board of the Royal Schools of Music.

Students with musical backgrounds will rather benefit from a harmony textbook, for example:

• Laitz, S.G. (2003). The complete musician: an integrated approach to tonal harmony, analysis, and listening. Oxford University Press.

• Gauldin, R. (1997). Harmonic practice in tonal music. Norton & Company.

For online introductions, see for instance:

• https://www.musictheory.net/lessons
• http://musictheory.pugetsound.edu/mt21c/MusicTheory.html

Learning Outcomes

By the end of the course, the student must be able to:

• Distinguish the core concepts used in digital musicology
• Explore and orient themselves in the multidisciplinary field and identify important research questions and methods
• Analyze databases containing musical and contextual data (e.g. corpora of pieces or metadata)
• Develop hypotheses about music and musical structures
• Assess / Evaluate their hypotheses with computational models
• Interpret results of their models in the context of the field
• Defend their research in discussion with peers

Transversal skills

• Set objectives and design an action plan to reach those objectives.
• Use a work methodology appropriate to the task.
• Assess progress against the plan, and adapt the plan as appropriate.
• Plan and carry out activities in a way which makes optimal use of available time and other resources.
• Communicate effectively, being understood, including across different languages and cultures.
• Take feedback (critique) and respond in an appropriate manner.
• Write a scientific or technical report.

Teaching methods

The course teaching consists of weekly lectures that will cover core topics, concepts and methods. In addition, it will include tutorials, research paper discussion and feedback on exercises.

Expected student activities

Students are expected to attend the class regularly and actively complete the exercises. Students are also required to fulfill the reading assignments.

Assessment methods

1.    Active participation in class.
2.    Exercises assigned during the class.

Supervision

Resources

Moodle Link

• https://go.epfl.ch/DH-401