Outline

Overview

This foundational unit will provide students with a broad understanding of the theoretical concepts of digital music. The unit includes a historical overview in the development of sound-based composition and creativity from its initial beginnings with the advent of sound recording through to the emergence of computer-based applications to create and organise sounds in time and space. The unit will focus on the approaches taken over the last century to record, transform and organise sound in a variety of creative contexts, ranging from early electroacoustic music experiments, through to contemporary electronic music production. Repertoire from the vast field of sonic arts will be covered to offer a new perspective on audio and sound production in today's contemporary music scene.

Unit details and rules

Academic unit	Composition and Music Technology
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Successful audition into Digital Music Composition program.
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Alexis Weaver, alexis.weaver@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Q&A following presentation, submission or placement	Viva Voce on Final Essay Viva voce oral assessment following submission of Final Essay.	5%	Formal exam period	10 minutes	AI prohibited
Outcomes assessed:
Out-of-class quiz	Early Feedback Task - Canvas quiz Canvas quiz based on content from unit outline + Week 1 and 2	0%	Week 03 Due date: 13 Mar 2026 at 23:59	5-10 minutes	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Listening test Aural assessment testing various works covered in class content and assigned homework.	25%	Week 05 Due date: 24 Mar 2026 at 23:59	1 hour	AI prohibited
Outcomes assessed:
Presentation	Production Analysis Presentation Present on and analyse a digital music technique, artist, or project of your choosing.	35%	Week 09 Due date: 28 Apr 2026 at 23:59	10-minute presentation.	AI allowed
Outcomes assessed:
Written work	Final Essay submission Write an essay investigating a particular musical style, compositional approach or technological development in the field of digital music composition.	35%	Week 13 Due date: 26 May 2026 at 23:59	2500 words	AI allowed
Outcomes assessed:
= early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Early Feedback Task: A mandatory, unweighted quiz based on content from the Unit of Study Outline and Week 1 and 2 lectures.

Listening test: Tests knowledge of repertoire covered during previous classes and in the recommended listening list on the class Canvas site. This test is designed for students to display their developing knowledge of the range of styles, aesthetics and
technological approaches present in the field of digital music and media.

Production Analysis Presentation: Students choose a piece, artist or technique to analyse for its innovative production techniques. The topic must be of your own choosing, and can be historical or contemporary. It must not have been covered in class or in the Canvas listening list. The presentation should break down the technical components of the work by any means necessary. It should include audio-visual examples as well as contextual/historical research on the techniques present in the work. You may choose to focus deeply on a subset of the techniques present in the work (e.g. a composer’s approach to spatialisation or use of synthesis techniques), or you may choose to look at the breadth of techniques present in the piece.

Final Essay: Choosing from a topic of interest from across the range of topics covered this semester, write an in-depth essay that investigates a particular musical style, compositional approach or technological development in the field of digital music and media. The essay should include substantial literature research of the topic in question, as well as musical analysis and historical context. You are allowed to use Generative AI in preparation for this task, however, you are required to demonstrate your knowledge of your topic, as well as your use of Generative AI in a 5-minute Q+A Viva Voce in Week 13 of Semester.

Q+A/Viva Voce Task: A 5-10 minute conversation demonstrating knowledge of your Essay topic and arguments. You will be asked questions about your writing and how it was composed. Your use of any AI software needs to be referenced in the Essay, and justified in the Viva Voce portion of the assessment.

Assessment criteria

The following assessment criteria guide the assessment of written work in this unit of study:

Result name	Mark range	Description
High distinction	85 - 100	Demonstrates high level of initiative in research and reading; sophisticated critical analysis of evidence; high level engagement with theoretical issues, innovative use of reading/research material and impressive command of underlying debates and assumptions; properly documented and written with style, originality and precision.
Distinction	75 - 84	Demonstrates initiative in research and wide, appropriate reading; complex understanding of question and ability to critically review material in relation to underlying assumptions and values; analyses material in relation to empirical and theoretical contexts; properly documented; clear, well-developed structure and argument with some signs of literary style.
Credit	65 - 74	Evidence of broader understanding than pass level; offers synthesis with some critical evaluation of material; coherent argument using a range of relevant evidence; some evidence of independent thought, good referencing. A high credit (70-74) shows some evidence of ability to problematise and think conceptually.
Pass	50 - 64	Written work meets basic requirements in terms of reading/research; relevant material; tendency to descriptive summary rather than critical argument; makes a reasonable attempt to avoid paraphrasing; reasonably coherent structure; often has weaknesses in particular areas, especially in terms of narrow or underdeveloped treatment of question; acceptable documentation.
Fail	0 - 49	Work may fail for any or all of the following reasons: Unacceptable paraphrasing; irrelevance of content; poor spelling; poor presentation; grammar or structure so sloppy it cannot be understood; failure to demonstrate understanding of content; insufficient or overlong word length.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Late penalties will be applied in accordance with section 7a of the University of Sydney "Assessment Proceedures 2011". Please refer to the Additional Information section for further instruction.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction: what is digital music? Global trends in digital music and media. From compositional aesthetics to contemporary digital cultures. Covering AI ethics and guidelines for tertiary study.	Lecture (1 hr)
Week 01	Working through activities associated with lecture content.	Tutorial (2 hr)
Week 02	Dreaming of the future - early electronic musical instruments.	Lecture (1 hr)
Week 02	Working through activities associated with lecture content.	Tutorial (2 hr)
Week 03	Sampling in the wild - the significance of sampling in contemporary music culture. History, techniques, aesthetics and ethics of sampling	Lecture (1 hr)
Week 03	Working through activities associated with lecture content.	Tutorial (2 hr)
Week 04	Sound synthesis - history, methods, and techniques	Lecture (1 hr)
Week 04	Working through activities associated with lecture content.	Tutorial (2 hr)
Week 05	Listening Test 1. In-class listening and continuation of sound synthesis learning.	Lecture (1 hr)
Week 05	Working through activities associated with lecture content.	Tutorial (2 hr)
Week 06	Sound processing and electronic production - audio effects and sound processing, history and techniques	Lecture (1 hr)
Week 06	Working through activities associated with lecture content.	Tutorial (2 hr)
Week 07	Mapping the digital - historical overview and theoretical concepts of digital audio (sampling theorem, bit depth, buffer sizes, non-real-time vs real-time audio)	Lecture (1 hr)
Week 07	Working through activities associated with lecture content.	Tutorial (2 hr)
Week 08	Shortcuts to Digital Sound - examining the role of presets and AI-driven tools in digital music production. Practical synthesis and sound design session	Lecture (1 hr)
Week 08	Working through activities associated with lecture content.	Tutorial (2 hr)
Week 09	Production Analysis Presentations. Practical Activities.	Lecture (1 hr)
Week 09	Working through activities associated with lecture content.	Tutorial (2 hr)
Week 11	DJ Culture and live electronic performance - development of performance practices, historical and contemporary tools and techniques	Lecture (1 hr)
Week 11	Working through activities associated with lecture content.	Tutorial (2 hr)
Week 12	Visualisation and sonification - synergies between sound, image, and data. Introduction to music and sound analysis and representation methods	Lecture (1 hr)
Week 12	Working through activities associated with lecture content.	Tutorial (2 hr)
Week 13	Essay Q&A/Viva Voce Assessment	Lecture (1 hr)
Week 13	Essay Q+A/Viva Voce Assessment	Tutorial (2 hr)

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.

Required readings

Students will be notified of readings in each relevant class.

Learning outcomes

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.

Outcomes
Graduate qualities

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

LO1. Recognise repertoire from various periods in the development of electronic and digital music
LO2. Demonstrate analytical skills and methodologies to describe sounds and non-traditional musical outputs
LO3. Demonstrate a clear understanding of the techniques used to create music in the digital domain
LO4. Demonstrate cultural competence to access available resources to explore new avenues in digital music production
LO5. Choose a modern digital music practice or cultural trend to analyse and evaluate

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
Learning outcomes

Responding to student feedback

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

Thank you to all students who took the time to fill out the Unit of Study survey for CMPN1220 (now CMPN1231 in 2026). I am so happy to see that students find this class worthwhile and interesting. Suggestions about diversifying the content delivery and breaking up the long class times will be taken on board for 2026. Thank you again for contributing to the future design of this class!

Additional information

More information will be shared in the course Canvas site.

Simple extensions

If you encounter a problem submitting your work on time such as an assignment, 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 or during the semester , 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, the USYD AI in Education Canvas Module 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.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

CMPN1231: Digital Music Composition 1

Semester 1, 2026 [Normal day] - Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links