Outline

Overview

The objective of this unit is to provide both a strong theoretical understanding of digital audio and practical experience in applying these principles to digital audio systems. This unit offers a systematic approach to understanding digital audio systems. Beginning with basic principles the unit provides a knowledge base for understanding advanced digital audio components, systems and techniques. Examples of everyday audio signals are used and characterised in terms of their temporal and spectral properties. Practical application is emphasised and is supported through laboratory exercises that include programming as well as the use of current hardware and software packages. Topics include: principles of digital signals and systems, sampling and quantisation, convolution, discrete Fourier transform (DFT), z-transform, transfer functions and impulse responses, finite and infinite impulse responses filtering, audio system design, time-variant systems, audio data compression and real-time audio DSP. Having successfully completed this unit the student will have the tools to understand what happens to a digital audio signal when a given process is applied to it; how to best apply this process and how to successfully combine digital audio components.

Unit details and rules

Academic unit	Architectural and Design Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Densil Cabrera, densil.cabrera@sydney.edu.au
Lecturer(s)	Ella Manor, ella.manor@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Assignment 2 written and design elements	50%	Formal exam period Due date: 28 May 2023 at 23:59	MATLAB project, report, presentation
Outcomes assessed:
Creative assessment / demonstration	Lab exercises Programming exercises	20%	Multiple weeks	n/a
Outcomes assessed:
Assignment	Assignment 1 Written and non-written elements	30%	Week 07 Due date: 09 Apr 2023 at 23:59	A set of exercises
Outcomes assessed:

Assessment summary

Assessments include regular lab exercises and two assignments. Assessments focus on digital audio system concepts, and MATLAB is used as a tool for this.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Work of outstanding quality, demonstrating mastery of the learning outcomes assessed. The work shows significant innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or exceptional skill.
Distinction	75 - 84	Work of excellent quality, demonstrating a sound grasp of the learning outcomes assessed. The work shows innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or superior skill.
Credit	65 - 74	Work of good quality, demonstrating more than satisfactory achievement of the learning outcomes assessed, or work of excellent quality for a majority of the learning outcomes assessed.
Pass	50 - 64	Work demonstrating satisfactory achievement of the learning outcomes assessed.
Fail	0 - 49	Work that does not demonstrate satisfactory achievement of one or more of the learning outcomes assessed.

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.

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:

This unit follows Sydney School of Architecture, Design and Planning policy for late penalties. https://www.sydney.edu.au/handbooks/architecture/rules/faculty_resolutions.shtml (1) It is expected that unless a simple extension, special consideration or disability adjustment has been granted, students will submit all assessment items for a unit of study on the due dates specified. If an assessment is submitted or otherwise completed within a period of extension granted by successful application for simple extension, special consideration or an assessment adjustment determined by Disability Services, no academic penalty will be applied to that piece of assessment. (2) If an extension is not sought, not granted, or is granted but work is submitted after the extended due date, the late submission of assessment will result in an academic penalty as follows: (a) For non-public assessments, work submitted after the deadline will incur a penalty of 5% of the total marks earned for the assessment per calendar day. Work submitted 20 calendar days or more after the deadline will not be assessed and will receive a mark of zero. (b) For assessments involving public, oral, and/or visual presentations completed after the deadline, a penalty of 10% of the total marks earned for the assessment will be imposed per calendar day. Work completed 10 calendar days or more after the deadline will not be assessed and will receive a mark of zero.

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.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to digital audio systems and signals	Lecture (1.5 hr)
Week 01	Introduction to MATLAB	Computer laboratory (1.5 hr)
Week 02	Introduction to sampling theory	Lecture (1.5 hr)
Week 02	Audio signal sampling decimation	Computer laboratory (1.5 hr)
Week 03	Introduction to Digital Signal Processing (DSP)	Lecture (1.5 hr)
Week 03	DSP in MATLAB	Computer laboratory (1.5 hr)
Week 04	Convolution	Lecture (1.5 hr)
Week 04	Simple reverb simulation	Computer laboratory (1.5 hr)
Week 05	Discrete Fourier Transform (DFT)	Lecture (1.5 hr)
Week 05	DFT and time segment processing	Computer laboratory (1.5 hr)
Week 06	Intro to the Z-Transform and Transfer functions and impulse responses	Lecture (1.5 hr)
Week 06	HRTF and HRIR	Computer laboratory (1.5 hr)
Week 07	Finite-Impulse-Response (FIR) filters	Lecture (1.5 hr)
Week 07	FIR filters and window functions	Computer laboratory (1.5 hr)
Week 08	Infinite-Impulse-Response (IIR) filters	Lecture (1.5 hr)
Week 08	Combining FIR and IIR filters to create artificial reverb	Computer laboratory (1.5 hr)
Week 09	Audio system design and sampling of musical instrument sound	Lecture (1.5 hr)
Week 09	LPC analysis and resynthesis	Computer laboratory (1.5 hr)
Week 10	Time-variant systems	Lecture (1.5 hr)
Week 10	Time delay modulation-based effects	Computer laboratory (1.5 hr)
Week 11	Audio data compression techniques	Lecture (1.5 hr)
Week 11	Audio data compression in MATLAB using DCT	Computer laboratory (1.5 hr)
Week 12	Real-time audio DSP	Lecture (1.5 hr)
Week 12	Introduction to audio DSP in Max/MSP	Computer laboratory (1.5 hr)
Week 13	Final Project Showcase	Lecture (3 hr)

Attendance and class requirements

Please refer to the Resolutions of the University School: http://sydney.edu.au/handbooks/architecture/rules/faculty_resolutions.shtml

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

Reading and electronic sources are accessible online or via the library’s website https://library.sydney.edu.au

Smith, S. W. (1999). The Scientist and Engineer's Guide to Digital Signal Processing(2nd ed.). Retrieved from https://www.analog.com/en/education/education-library/scientist_engineers_guide.html

Rao, K. D. (2018). Digital Signal Processing Theory and Practice. Singapore: Springer Singapore.

McLoughlin, I. V. (2016). Speech and Audio Processing: A MATLAB-based Approach. Cambridge University Press.

Zölzer, U. (2011). Dafx: Digital Audio Effects (2nd ed.): John Wiley & Sons.

Pohlmann KC. (1989) Principles of digital audio. Sams; 1989.

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. use the terminology associated with the digital sampling, processing, and reconstruction of analog audio signals. Demonstrate a basic knowledge of Sampling Theory, including audio signal sampling-rate conversion, and be able to argue for the suitability of particular solutions for storage and distribution of audio signals. Demonstrate knowledge of human auditory perception in the contexts of audio system design and Digital Signal Processing (DSP)
LO2. understand and be able to apply the Discrete Fourier Transform to time domain signals to determine their frequency composition. Demonstrate knowledge of Convolution and be able to correctly derive Transfer Functions and Impulse Responses from the analysis of audio systems
LO3. understand audio system design, and particularly two types of digital filters: the Finite-Impulse-Response (FIR) filter and the Infinite-Impulse-Response (IIR) filter
LO4. understand the analysis and design of Linear Time-invariant Systems related to the simulation of sound propagation through space
LO5. understand modern forms of audio data compression and demonstrate knowledge of advanced audio coding algorithms for storage and distribution of audio content
LO6. show familiarity with new areas of work involving digital audio systems, particularly in advanced applications development.

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.

Numerous comments made by students requested an opportunity to present the practical components of Assignment 2 to their peers. This year we will give students the opportunity to showcase their projects in the last class meeting (week 13). No other changes have been made since this unit was last offered.

Additional information

Additional costs

There are no additional costs for this unit.

Site visit guidelines

There are no site visit guidelines for this unit.

Work, health and safety

There are no specific work health and safety requirements for this unit.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Additional costs

Site visit guidelines

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

DESC9115: Digital Audio Systems

Semester 1, 2023 [Normal evening] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Additional costs

Site visit guidelines

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect