MUSONIC 2310 - Computer Music Composition 2
North Terrace Campus - Semester 2 - 2017
General Course Information
Course Code MUSONIC 2310 Course Computer Music Composition 2 Coordinating Unit Elder Conservatorium of Music Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Prerequisites MUSONIC 1000, MUSONIC 1220 Assumed Knowledge Basic understanding of the role of technology in the creation and performance of music Course Description This course examines of the link between human-computer interaction and the creative and technical practice of sound and music making. This course will develop a theoretical and practical understanding of computer music composition. Focus is placed on acquiring programming skills for implementation of compositional algorithms. Students will engage with a number of topics, including conceptual frameworks, contemporary practices and practitioners; complete readings and listenings; and perform practical exercises that promote investigative learning and research. The course has the following learning objectives: facilitate new understandings and exploratory approaches in sonic arts practice; extend knowledge and develop new artistic and technical skills in human computer interaction and sound and music; and promote a learning process and reflexive skill set with regard to future practice, thus enabling students to adapt to the ever expanding and rapidly changing area of sonic arts and related areas of computer music.
Course Coordinator: Mr Stephen Whittington
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes
The objectives of this course are:
1. to develop an awareness of the historical context in which computer-assisted composition evolved and its
development to the present day
2. to develop an understanding of the underlying principles of computing and music viewed as formal systems
3. to develop an understanding of the various ways in which computers can assist in the process of musical composition
4. to enhance problem solving skills in the field of computer composition
5. to allow students to explore creative methods using computers, leading to the composition of musical works
Learning outcomes are:
(1) Knowledge of the historical context of computer music
(2) Ability to analyse formal systems and apply them to music
(3) Ability to apply algorithmic methods to musical composition
(4) Ability to write programs to realise compositions with algorithmic structures
(5) Ability to solve problems in programming and implementation
(6) Ability to distinguish between technical and aesthetic aims, and to be able to articulate both aspects of a project
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1,2 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
2, 3, 4, 5 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
3,4 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- Able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
6 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Online LearningExtensive reading, online tutorials, web links, discussion forums and other material is available online.
Learning & Teaching Activities
Learning & Teaching ModesThis course is taught through workshops focussing on programming, and seminars focussing on composition al applications.
No information currently available.
Learning Activities Summary
1: Machine Music
2: The history of programmable machines (Jacquard looms, automata etc); Babbage’s Difference and Analytical Engines;
Turing machines; universal computers; the limits of computation; indeterminacy
and incompleteness; the Turing test applied to music.
3: Music and mathematics; music, mathematics, geometry and logic as formal systems or abstract
systems of thought; logical operations; weird computers; symmetry and form;
group theory; pitch and temporal symmetry; the symmetry of scales.
4: Probability and random processes: randomness and pseudo-randomness; probability
distributions; conditional probabilities; John Cage and I Ching computation.
5: Simple algorithmic procedures applied to composition; deterministic and probabilistic
6: The physical and psycho-acoustic foundations of tuning; frequency vs. pitch; pitch as a
dimension of timbre; quantum indeterminacy and the limits of tuning; the
implementation of tuning systems in synthesizers and comput
7: Composition as process: rule-governed composition from ancient models to the
8: The early history of computer music composition. Early programming languages: MUSIGOL,
MUSIC IV, MUSIC V; Lejaren Hiller: Illiac Suite. James Tenney: Stochastic Quartet. Charles Dodge: Earth’s
Magnetic Field. Xenakis: SMP (Stochastic Music Program.)
9: Markov models in computer music.
10: Generative grammar in computer music.
11: Artificial neural networks and artificial intelligence in computer music.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment SummaryAssessment is based on completion of exercises and submission of a major project with associated documentation.
a. Major project (composition) 50% Learning Objectives: 1,2,4,5
b. Research project 30% Learning Objectives: 1,3,4
c. Programming exercises 20% Learning Objectives: 4,5
Assessment Related Requirements
Attendance Expectation & Penalty
Students are expected to attend all workshops. If a student fails to attend at least 70% of workshops in a course and fails to produce the appropriate medical or compassionate certificates, the student is deemed to have failed that course, irrespective of assignments previously completed. Students who arrive 10 minutes or later after the start of a class will be marked as absent.
The major project will be a composition implementing ideas presented during this course. It should be realised using the SuperCollider programming language. The composition must demonstrate a clear understanding of major concepts, and
include both the use of algorithmic composition procedures, and the use of a tuning system other than equal temperament. Due date (final version): Friday June 19 at 12 midnight.
The seminar presentation (Weeks 12-13) will be an opportunity for students to present to the class their major project, which by this stage should be in an advanced stage of development, although not necessarily in its final form. The presented
should demonstrate the ways in which the composition meets the criteria outlined in (1).
Student work will be supported by a MyUni blog. The blog should reflect on ideas presented in seminars, workshops, readings and
online tutorials. A primary function of the blog is to report on the research and development of the major project and document its creation. Discussion of the evolution of the underlying concept of the work, its relationship to ideas and techniques presented in class, reports on problems encountered and solutions found, uploading of work in progress, and other related material is
expected. At a minimum, students should make one blog entry per week for the duration of the course. Students are also encouraged to read other students blogs and comment appropriately on them.
Programming exercises are progressive exercises developing skills in specific programming techniques which will be completed throughout the semester. Due dates for will be given through the semester.
The research project will be an essay of 1500 words on a topic relevant to this course. The project may be related to a composer, an approach to computer-assisted composition, a specific example of computer music, or to a philosophical topic related to computer music. It is expected that this research will also inform the major project composition.
No information currently available.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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This section contains links to relevant assessment-related policies and guidelines - all university policies.
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