COMPGC20 - Computer Music
Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).
|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:email@example.com> 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).
Ethical Issues and Evaluation of Music Systems
Audio and Sampling
Introduction to Tonal Music Theory
Symbolic representations of music (MIDI, Kem, MusicXML)
Music Information Retrieval
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;
- Obtain a minimum mark of 40% in each component worth ≥ 30% of the module as a whole.
The Computer Music Tutorial, Curtis Roads, MIT Press, 1996. ISBN 0262680823.
Introduction to Computer Music, Nick Collins, Wiley, 2009, ISBN 978-0-470-71455-3.
Musimathics, vol. 1, Gareth Loy, MIT Press, 2006, ISBN 0262122820.
Musimathics, vol. 2, Gareth Loy, MIT Press, 2007, ISBN 0262122855.
Artistic and Historical Aspects
The Cambridge Companion to Electronic Music, Nick Collins & Julio d’Escrivan, Cambridge University Press, ISBN 9780521688659.
Virtual Music, David Cope, MIT Press, 2001, ISBN 026203283X.
Computer Models of Musical Creativity, David Cope, MIT Press, 2006, ISBN 0262033380.
Machine Musicianship, Robert Rowe, MIT Press, 2004, ISBN 0262681498.
Algorithmic Composition, Mary Simoni & Roger Dannenberg, 2011, forthcoming
Algorithmic Composition: Paradigms of Automated Music Generation, Gerhard Nierhaus, Springer, 2009, ISBN 978-3-211-75539-6.
Signal Processing Methods for Music Transcription, Anssi Klapuri & Manuel Davy (eds), Springer, 2006, ISBN 978-0-387-30667-4.
Music and Probablity, David Temperley, MIT Press, 2007, ISBN 0-262-20166-6.
The Cognition of Basic Musical Structures, David Temperley, MIT Press, 2002, ISBN 0262201348.
Musicology: The Key Concepts, David Beard & Kenneth Gloag, Routledge, 2005, ISBN 0415316928.
Pure Data website, http://puredata.info
Pure Data, http://en.flossmanuals.net/PureData
Programming Electronic Music in Pd, Johannes Kreidler, Wolke Publishing, 2009, ISBN 3936000573.
Journal and conference papers will also be used to supplement these texts.
Further information and recommended resources may be found on the Moodle pages for this course: https://moodle.ucl.ac.uk/course/view.php?id=14712