ELEC5305: Semester 2, 2025

Units ELEC5305 Semester 2 2025 [Normal day]

Unit outline_

ELEC5305: Acoustics, Speech and Signal Processing

Semester 2, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

The course is designed to meet the needs of the increasing demand for advanced signal processing in the areas of acoustics and speech, biology and medicine, sonar and radar, communication and networks. Modern systems typically incorporate large sensor arrays, multiple channels of information, and complex networks. The course will cover topics in compressed sensing, multiresolution analysis, array signal processing, and adaptive processing such as kernel recursive least squares. The course will develop concrete examples in areas such as microphone arrays and soundfield analyses, medical signal processing, tomography, synthetic aperture radar and speech and audio. The concepts learnt in this unit will be heavily used in various engineering applications in sensor arrays, wearable medical systems, communication systems, and adaptive processing for complex financial, power, and network systems. The Defense, Science, and Technology Organisation will contribute to this course with teaching support and data.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	(ELEC2302 or ELEC9302) and (ELEC3305 or ELEC9305). Linear algebra, fundamental concepts of signals and systems as covered in ELEC2302/ELEC9302, fundamental concepts of digital signal processing as covered in ELEC3305/9305. It would be unwise to attempt this unit without the assumed knowledge- if you are not sure, please contact the instructor
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Craig Jin, craig.jin@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Final Exam Final Exam	20%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Contribution	Learning Topics/Quizzes Discussion of Weekly Topics	5%	Multiple weeks	30 minutes	AI allowed
Outcomes assessed:
Practical skill	Audio Processing Report One Coding application report using MATLAB Livescript	15%	Week 04 Due date: 31 Aug 2025 at 23:59 Closing date: 07 Sep 2025	One MATLAB Livescript Report	AI allowed
Outcomes assessed:
Practical skill	Audio Processing Report Two Coding application report using MATLAB Livescript	15%	Week 08 Due date: 28 Sep 2025 at 23:59 Closing date: 05 Oct 2025	One MATLAB Livescript Report	AI allowed
Outcomes assessed:
Practical skill	Audio Processing Report Three Coding Application Report using MATLAB Livescript	15%	Week 12 Due date: 02 Nov 2025 at 23:59 Closing date: 09 Nov 2025	One MATLAB Livescript Report	AI allowed
Outcomes assessed:
Research analysis	Research project Project work	30%	Week 13 Due date: 09 Nov 2025 at 23:59 Closing date: 16 Nov 2025	10 Pages	AI allowed
Outcomes assessed:

Assessment summary

Coding Modules: create signal processing models for a task using Matlab/Python
Discussion: Develop a Project Research Question and Complete a Literature Review and Explore Concepts
Research Project: Develop and Work on an audio signal processing project

Assessment criteria

Result Name	Mark Range	Description
Coding Modules	0-10	Code should mostly work and solve the task to get a credit mark
Project Work	0-100	A research question in the area of audio signal processing should be developed based on analysis of existing research and literature. Your project work should include code and test experiments which help address and answer your research question. Your research question should be supported by recent literature and your project work should provide some insights and answers into your research question to receive a credit mark.
Final Exam	0-100

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:

There will be a 5% late penalty.

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 to working with audio using MATLAB	Lecture and tutorial (3 hr)
Week 01	Introduction to working with audio using MATLAB	Practical (2 hr)
Week 02	Basic Audio Signal Analysis - WIndowing, Filtering, Pre-Emphasis	Lecture and tutorial (3 hr)
Week 02	Basic Audio Signal Analysis - WIndowing, Filtering, Pre-Emphasis	Practical (2 hr)
Week 03	Simple Sound Synthesis and Audio Features	Lecture and tutorial (3 hr)
Week 03	Simple Sound Synthesis and Audio Features	Practical (2 hr)
Week 04	Short-Time Fourier Transform	Lecture and tutorial (3 hr)
Week 04	Short-Time Fourier Transform	Practical (2 hr)
Week 05	Modulation Spectrum	Lecture and tutorial (3 hr)
Week 05	Modulation Spectrum	Practical (2 hr)
Week 06	Cochlear Filter Banks	Lecture and tutorial (3 hr)
Week 06	Cochlear Filter Banks	Practical (2 hr)
Week 07	Audio Processing Examples: Voice Activity Detection and Wiener Masks	Lecture and tutorial (3 hr)
Week 07	Audio Processing Examples: Voice Activity Detection and Wiener Masks	Practical (2 hr)
Week 08	Linear Prediction and Source Filter Models	Lecture and tutorial (3 hr)
Week 08	Linear Prediction and Source Filter Models	Practical (2 hr)
Week 09	Putting it all together - Stereo Decomposition Weights	Lecture and tutorial (3 hr)
Week 09	Putting it all together - Stereo Decomposition Weights	Practical (2 hr)
Week 10	Putting it all together - Stereo Decomposition (Ambient and Direction Separation)	Lecture and tutorial (3 hr)
Week 10	Putting it all together - Stereo Decomposition (Ambient and Direction Separation)	Practical (2 hr)
Week 11	HRIRs and BRIRs and Spatial Mixing	Lecture and tutorial (3 hr)
Week 11	HRIRs and BRIRs and Spatial Mixing	Practical (2 hr)
Week 12	Beamforming	Lecture and tutorial (3 hr)
Week 12	Beamforming	Practical (2 hr)
Week 13	Review	Lecture and tutorial (3 hr)
Week 13	Review (Assistance with Project)	Practical (2 hr)
Weekly	Independent Study (Reading material, online videos, weekly quizzes, etc)	Independent study (4.25 hr)

Attendance and class requirements

Attendace and Participation is required in all course activities.

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

There is no specified textbook for this course. Material will be taken from a number of books and research papers. Below is a list of some of the reference books we will be using.

Title: Speech and Audio Signal Processing

Authors: Ben Gold, Nelson Morgan, Dan Ellis

Publisher: Wiley

Publish date: 2011

Title: Sound Capture and Processing

Authors: Ivan Tashev

Publisher: Wiley

Publish date: 2009

Title: Auditory Neuroscience

Authors: Jan Schnupp, Israel Nelken, Andrew King

Publisher: MIT Press

Publish date: 2011

Title: Parametric Time-Frequency Domain Spatial Audio

Editors: Ville Pulkki, Symeon Delikaris-Manias, Archontis Politis

Publisher: Wiley

Publish date: 2018

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. Describe audio signals using mathematical concepts such as convolutions, transforms, spectral analyses, Short-Time Fourier Transforms, Correlation and Covariance
LO2. Use software, such as MATLAB and Python, to develop code to analyse and process audio signals including filtering, inverse filtering, spectral analyses, direct-ambient separation, 3D spatialisation, audio synthesis, speech models
LO3. Identify audio signal models such as source filter models, sine wave modelling, Gaussian mixture models, ICA models, Deep Network Sequence-to-Sequence models, Wiener filtering models, Linear Predictive models
LO4. Design and Evaluate signal processing systems.

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.

We have redesigned the curriculum flow to be more direct and accessible to students.

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

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

Disclaimer

On this page

Useful links