ELEC5305: Semester 2, 2024

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Units ELEC5305 Semester 2 2024 [Normal day]

Unit outline_

ELEC5305: Acoustics, Speech and Signal Processing

Semester 2, 2024 [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	Electrical and Information 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
Lecturer(s)	Craig Jin, craig.jin@sydney.edu.au

Assessment

The census date for this unit availability is 2 September 2024

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Final Exam Final Exam	20%	Formal exam period	2 hours
Outcomes assessed:
Participation	Developing a Research Question and Analysis of Literature Developing Project Research Question with Literature Review	10%	Multiple weeks	During Discussion Session
Outcomes assessed:
Small continuous assessment	Signal Processing Coding Practice Signal Processing coding in MATLAB/Python	40%	Multiple weeks	During Tut/Lab
Outcomes assessed:
Assignment	Research Project Project work	30%	Week 11 Due date: 16 Oct 2023 at 23:59	20 hours across semester
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.

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 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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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 2023 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 2023. 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	Human hearing, Filter Banks, Spectrograms	Block teaching (5 hr)
Week 02	Human Hearing, Filter Banks, Specgrograms	Block teaching (5 hr)
Week 03	Audio Features	Block teaching (5 hr)
Week 04	Basic Machine Learning	Block teaching (5 hr)
Week 05	Deep Networks and Audio Signal Processing	Block teaching (5 hr)
Week 06	Deep Networks and Audio Signal Processing	Block teaching (5 hr)
Week 07	Deep Networks and Audio Signal Processing	Block teaching (5 hr)
Week 08	Spatial Audio and Stereo Decomposition	Block teaching (5 hr)
Week 09	Spatial Audio and Stereo Decomposition	Block teaching (5 hr)
Week 10	Microphone Arrays and Dereverberation	Block teaching (5 hr)
Week 11	Deep Networks and Speech	Block teaching (5 hr)
Week 12	Deep Networks and Speech, Compressed Sensing and/or Catch-Up	Block teaching (5 hr)
Week 13	Review	Block teaching (5 hr)

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. Demonstrate mastery of analytical and mathematical skills related to acoustic signal processing. These include short-time frequency transform, filter bank processing, microphone array processing, sound field analysis and synthesis.
LO2. Demonstrate proficiency in developing signal processing software to solve signal processing problems and tasks. These include direct-ambient separation, auditory modelling, spatial sound analysis and synthesis, deep learning models.
LO3. Plan, design, and review signal processing systems.
LO4. Apply diverse strategies to develop and implement innovative ideas in signal processing systems.
LO5. Present compelling oral, written, and graphic evidence to communicate signal processing practice.
LO6. Contribute as an individual to teams to deliver signal processing related projects.

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.

This is a new course.

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

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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