MUSS2002 Music & Computing 2 (5 op)

This course introduces data-driven approaches to music-related analysis, focusing on computational methods used in music information science.

Students gain hands-on experience working with real-world datasets and learn to extract, interpret, and visualize relevant features using programming environments and specialized toolboxes. Core analytical techniques such as feature extraction, classification, and clustering are explored through lectures and practical assignments.

Students develop an independent research project throughout the course, culminating in a final presentation and written report. Emphasis is placed on both technical skill development and creative problem-solving. The skills acquired are applicable to areas such as music cognition, music and movement, music education, and music therapy.

After completing the course, the student:

· understands and can explain the fundamental concepts in music information science

· is able to apply data-driven analytical methods such as correlation, regression, classification, and clustering in the context of music and movement

· can independently use MATLAB for processing, analyzing, and visualizing data

· can write original computer scripts and functions that can be integrated into existing open-source software and/or toolboxes

· is capable of designing and executing a small-scale research project that involves the computational analysis of music and/or movement data

· can critically reflect on methodological choices and outcomes in computational music research, and communicate findings effectively through presentations and written report.

Timing: 1st or 2nd year.

Study material supplied by teachers