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COMP4221: ADVANCED MUSIC COMPUTING

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Type	Open
Level	4
Credits	20
Availability	Available in 2024/2025
Module Cap
Location	Durham
Department	Computer Science

Prerequisites

COMP3717: Introduction to Music Processing. Students do not need to have theoretical or practical expertise in music beyond what is developed and used in COMP3717

Corequisites

None

Excluded Combinations of Modules

None

Aims

This course develops advanced understanding and skills for computational modelling, analysis, creation and performance of music. The focus will be on the practical application of recent developments in the field.

Content

Mathematical and computational models of musical structures and features
Automated analysis of individual and collected pieces of music e.g. in setlists, albums, published collections and corpora
Creative practices in computational music, including algorithmic processes for creation/composition of music and live coding
Examples of computational techniques and software used within popular and classical music

Learning Outcomes

Subject-specific Knowledge:

On completion of the module, students will be able to demonstrate:
Understanding of recent developments in computational techniques for representing, manipulating, analysing and creating music
Advanced knowledge of software tools used in computational music practice

Subject-specific Skills:

On completion of the module, students will be able to demonstrate:
The use of recent software tools to perform automated analyses of pieces or collections of music
The creation of music using advanced computational tools

Key Skills:

On completion of the module, students will be able to demonstrate:
Analysis of large and disparate data sets
Selection and application of mathematical and computational concepts in a specific domain

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

Lectures introduce specific topics, primarily from recent developments in the field (both academic reports and major code base updates).
Practicals enable students to experiment with the topics at hand. Each practical serves as the basis for a possible final submission (see summative assessment).
Summative assessment involves student-proposed/selected projects (individual or group) to create artefacts related to computational music: tools, analyses, compositions or performances. Assessment will be based on techniques used, not artistic merit.

Teaching Methods and Learning Hours

Activity	Number	Frequency	Duration	Total	Monitored
lectures	20	1 per week	1 hour	20
workshops	20	1 per week	1 hour	20	Yes
preparation and reading				160
Total				200

Summative Assessment

Component: Coursework		Component Weighting: 100%
Element	Length / Duration	Element Weighting	Resit Opportunity
Computational music project		100

Formative Assessment

Formative assessment involves presenting work in progress to the group, to receive peer review and instructor review on the feasibility, novelty, and value of the proposed project.

More information

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