Program Overview

Master in Sound and Music Computing

Overview

The Master in Sound and Music Computing is a program that introduces students to the analysis of music using symbolic representations, focusing on their central role in computational musicology.

Academic Program

The program includes a course on Symbolic Music Analysis and Computational Musicology, which covers the following topics:

• Symbolic Music Representation: Introduction to symbolic representations of music, in which structural elements such as notes, chords, and rhythms are encoded as discrete, machine-readable data.
• Foundations of Music Theory: Study of the basic elements of Western music theory, including intervals, scales, chords, rhythm, and metre, providing the foundation for the musicological topics addressed in the course.
• Introduction to Musicology: Survey of the scope and methods of musicology, from its historical roots to contemporary practices. Topics include the discipline’s central aims, research strategies, and its intersections with other areas of music research.
• Introduction to Ethnomusicology: Overview of ethnomusicology as a discipline, addressing its historical development, theoretical foundations, and research methods. Emphasis is placed on the implications and challenges of symbolic encoding of non-Western music traditions.
• Computational Methods in (Ethno)Musicology: Exploration of algorithmic and statistical approaches applied to symbolic corpora to support musicological and ethnomusicological inquiry.
• Practical Introduction to music21: Hands-on sessions with music21, a Python toolkit for computer-aided musicology, with exercises ranging from basic parsing to more advanced analytical applications.
• Musicology-informed MIR: Analysis of Music Information Retrieval approaches that integrate concepts from music theory and musicology, showing how domain knowledge can enhance the design, interpretation, and evaluation of MIR systems.
• Large-Scale Symbolic Corpora and Symbolic Language Models: Examination of large symbolic datasets, their construction and curation, and emerging applications of symbolic large language models (LLMs) for analytical and musicological tasks.

Format

The course is offered in 10 weeks during the 2nd Term (January-March), with 25 hours of lectures. The class format includes 2.5 hours per week of lectures, hands-on exercises, and discussions.

Evaluation

Evaluation consists of:

• In-class presentation (20%)
• Research paper assignment (70%)
• Peer review assignment (10%)

Materials and References

• Main software: music21
• Programming language: Python
• Additional tools: MuseScore