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Unit outline_

CMPN2632: Computer Music Fundamentals

Semester 2, 2020 [Normal day] - Sydney

This unit will focus on computer based music composition utilising formal processes to create music with computers. This unit investigates an interactive graphical programming environment for music, audio and media called Max, allowing the composer to program and implement interactive music processes quickly and with varying levels of sophistication, thus encouraging the student to explore real-time creative possibilities of digital music technology.

Unit details and rules

Academic unit Composition and Music Technology
Credit points 6
Prerequisites
? 
MUED1002 OR CMPN1013 OR MUSC2653
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Ivan Zavada, ivan.zavada@sydney.edu.au
Type Description Weight Due Length
Participation In-class tasks
Small exercises
20% Ongoing Ongoing
Outcomes assessed: LO1 LO3 LO2
Assignment Assignment 1
Design an automated note player for a two-voice free counterpoint
20% Week 06 Max Patch
Outcomes assessed: LO1 LO2 LO3
Assignment Assignment 2
Create a synthesizer using two modulation techniques
20% Week 10 Max Patch
Outcomes assessed: LO1 LO2 LO3
Assignment Final project
Final project
40% Week 12 Max Patch and 500 word documentation
Outcomes assessed: LO1 LO2 LO3

Assessment summary

  • Assignment 1: Design an automated note player for a two-voice free counterpoint consisting of repeated motifs with varying intervals.
  • Assignment 2: Create a synthesizer using at least two of the modulation techniques viewed in class: additive synthesis, amplitude modulation, ring modulation, frequency modulation, wavetable synthesis.
  • In-class tasks: Small exercises will be done during tutorial times and submitted via Canvas in class for evaluation on a regular basis during the semester. 4 tasks will be assessed individually to make up the mark for participation
  • Final project: Program a MIDI or AUDIO effects processor which emulates an effect or synthesizer.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The following assessment tasks and due dates for this unit of study are as follows:
Assignments – 40%
Final Project – 40%
Participation – 20% (in-class tasks and participation)

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction to Max/MSP and overview of MIDI Online class (2 hr) LO1
Week 02 Introduction to sound synthesis and signal processing Online class (2 hr) LO1 LO2
Week 03 Programming with max conditions, debugging, simple objects – counter, delay, gate Online class (2 hr) LO1
Week 04 Sound synthesis – Generators and LFOs Online class (2 hr) LO1
Week 05 Sound synthesis – Modulation Online class (2 hr) LO1
Week 06 Filters Online class (2 hr) LO1
Week 07 Random processes and stochastics, data manipulation Online class (2 hr) LO1
Week 09 Project programming online group discussion Online class (2 hr) LO3
Week 11 Creating your own software in Max Online class (2 hr) LO3
Week 12 Discussion and presentation - final projects/compositions Online class (2 hr) LO3
Weekly 1 hour lab class per week with online consultation Independent study (12 hr) LO3

Attendance and class requirements

  • Attendance: Students are expected to attend a minimum of 90% of timetabled activities for a unit of study, unless granted exemption by the Dean, Head of School or professor most concerned. The Dean, Head of School or professor most concerned may determine that a student fails a unit of study because of inadequate attendance. Alternatively, at their discretion, they may set additional assessment items where attendance is lower than 90%.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. explore concepts of interactive music performance and integrate these concepts into your own creative practice
  • LO2. acquire a thorough understanding of the MIDI protocol and be aware of other similar environments (Open Sound Control, Processing, CSound)
  • LO3. effectively implement musical ideas within the Max/MSP/Jitter music, audio and media programming environment.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

NO INFORMATION ENTERED HERE YET

Disclaimer

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