MUSONIC 3310 - Computer Music Composition 3

North Terrace Campus - Semester 2 - 2017

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.

  • General Course Information
    Course Details
    Course Code MUSONIC 3310
    Course Computer Music Composition 3
    Coordinating Unit Elder Conservatorium of Music
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 4 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites MUSONIC 1000
    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 Staff

    Course Coordinator: Mr Stephen Whittington

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    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
    Teamwork and communication skills
    • developed from, with, and via the SGDE
    • honed through assessment and practice throughout the program of studies
    • encouraged and valued in all aspects of learning
    3,4
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    6
    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
    6
  • Learning Resources
    Online Learning


    Extensive reading, online tutorials, web links, discussionforums and other material are available online.

  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course is taught through workshops in programming, and seminars in composition.
    Workload

    No information currently available.

    Learning Activities Summary


    ·  Topics:
    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
    algorithms.



    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
    modern era.

     

    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.

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary

    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.

    Assessment Detail


    1.      
    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.
     

    2.      
    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).

     

    3.      
    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.

     

    4.      
    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.

     

    5.      
    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.

    Submission

    No information currently available.

    Course Grading

    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.

  • Student Feedback

    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.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

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