Interdisciplinary Music Computing

Course goals

Course goals

After completion of the course, the student:

Content

Content

Today, many of our music creation, dissemination, and listening practices are shaped by computational technologies. From digital audio workstations (DAWs), to beat mapping in DJ software, dynamic audio processing in video games, and music streaming recommendations, music computation plays an important role in a wide variety of music and media contexts. 

In academic research, the meeting of musicology and computing science opens new ways of understanding music across many genres and contexts. Computational approaches support the exploration of patterns in large music corpora, the analysis of performances and performance decisions, engagements with digitized archival materials, understandings of listening behavior, and the development of music-based therapeutic interventions. 

This interdisciplinary course focuses on how computational methods can contribute to music research and to the design and use of applications and tools both in academic and professional contexts. The course is offered both to Master students with a Science background and Master students with a Humanities background. The course offers the opportunity to collaborate with other students from said disciplines, bringing in different paradigms and methodologies. Collaboration across disciplines is essential for developing, applying, and critically evaluating tools and models that are both technically robust and sensitive to music’s many social and cultural contexts. In this course, students explore therefore not only how interdisciplinary collaboration works, but also why it matters. 

The course consists of lectures and teamwork. In the lectures, core concepts will be introduced. Students will work in interdisciplinary teams on applying computational models to open-ended, real-world challenges in the context of music computing. We explicitly will reflect on the interdisciplinary interaction within the teams, and on the process of modelling and tool-building.

As a student with a background in science, you will critically reflect on the positioning of computational methodologies in wider academic and societal contexts. Through collaboration with musicology students, you will gain a deeper understanding of the possibilities and challenges for developing computational approaches, and a stronger understanding of data-in-context. Working at the disciplinary boundary between computing science and musicology prepares you to contribute meaningfully to the ongoing development, assessment, and responsible implementation of technologies that shape music and media technology today.

As a student with a background in humanities, you will obtain a deeper understanding of the limitations and possibilities of computational approaches in relation to the study of music. By learning the foundational principles of the computer as a machine, you will gain a profound basis to understand the affordances of tools. You will get an overview of how tools and models are generally evaluated within design science, allowing you to assess the value of a given tool concerning its fitness for a purpose.

Instructional Modes

Lectures, teamwork, presentations.

Tests 

Individual Portfolio

Test weight: 100

Minimum grade: 5.5