COMPGC20 - Computer Music
This database contains 2016-17 versions of the syllabuses. For current versions please see here.
|Prerequisites||The course has no formal prerequisites but involves programming in graphical and textual languages, and engagement with musical concepts. No prior knowledge of programming is assumed and the languages used are taught from first principles, however, those with no or very little computing experience of any kind may find the learning curve quite steep! Thus some basic knowledge of programming in any language will be helpful. On the musical side, it will help if you have some basic knowledge of music but again, this can be learned from books and will be briefly taught on the course if needed. Reviewing this page <www.cs.cmu.edu/%7Emusic/cmsip/readings/music-theory.htm> may give some idea of the knowledge required. Prospective students are encouraged to contact the lecturer <mailto:firstname.lastname@example.org> to discuss their suitability.|
|Taught By||Nicolas Gold (100%)|
|Aims||To provide students with a grounding in the state of the art in computer music theory, technology and applications.|
Students successfully completing this module should be able to:
The course will cover three key aspects of computer music: representation, creativity and analysis addressed through theory and practice. It will cover sound, music as organised sound, and specific applications (e.g. music information retrieval and musicology). Students will be strongly encouraged to explore both scientific and artistic aspects of the course through programming exercises to generate sound and music in contemporary visual or textual music and arts programming languages (e.g. Python and Pure Data).
Audio and Sampling
Introduction to Tonal Music Theory
Symbolic representations of music (MIDI, MusicXML)
Music Information Retrieval
Ethical Issues and Evaluation of Music Systems
Method of Instruction:
Lectures, laboratories, and demonstrations of techniques. Students will be given weekly exercises to explore and practice techniques. Reading and listening recommendations will also be provided via Moodle.
The course has the following assessment components:
- Unseen written examination (2 hours, 90%);
- Algorithmic composition (audio file, program notes, source code) (10%).
To pass this module, students must:
- Obtain an overall pass mark of 50% for all components combined.