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Unit of study_

ELEC5305: Acoustics, Speech and Signal Processing

Semester 2, 2022 [Normal day] - Camperdown/Darlington, Sydney

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

Unit code ELEC5305
Academic unit Electrical and Information Engineering
Credit points 6
Prohibitions
? 
None
Prerequisites
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None
Corequisites
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None
Assumed knowledge
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(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
Type Description Weight Due Length
Online task Research Paper Analysis
Critical analysis and summary of research paper
20% Multiple weeks During Discussion Session
Outcomes assessed: LO5
Online task Signal Processing Coding Practice
Signal Processing coding in MATLAB/Python
50% Please select a valid week from the list below During Tut/Lab
Outcomes assessed: LO1 LO6 LO2
Assignment Something Awesome Project
Project work
30% Week 12
Due date: 30 Oct 2022 at 23:59
20 hours across semester
Outcomes assessed: LO3 LO4

Assessment summary

 

  • Coding Practice: create signal processing code for a task using Matlab/Python
  • Research Paper Analysis: provide a critical analysis and summary of a research paper
  • Something Awesome Project: develop and work on an audio signal processing project

 

Assessment criteria

Result Name Mark Range Description
Coding Practice 0-10 Code should mostly work and solve task to get a credit mark
Research Paper Analysis 0-10 Provide a critical analysis of the research paper indicating what is the research contribution, method and results and what aspects are good and what aspects can be criticized.
Something Awesome Project 0-100 Project code, report and video must be submitted demonstrating the solving of a signal proessing task to obtain a credit mark.

 

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.

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 Human hearing, Filter Banks, Spectrograms Block teaching (5 hr) LO1 LO2 LO6
Week 02 Human Hearing, Filter Banks, Specgrograms Block teaching (5 hr) LO1 LO2 LO6
Week 03 Audio Features Block teaching (5 hr) LO1 LO2 LO6
Week 04 Basic Machine Learning Block teaching (5 hr) LO1 LO2 LO6
Week 05 Deep Networks and Audio Signal Processing Block teaching (5 hr) LO1 LO2 LO6
Week 06 Deep Networks and Audio Signal Processing Block teaching (5 hr) LO1 LO2 LO6
Week 07 Deep Networks and Audio Signal Processing Block teaching (5 hr) LO1 LO2 LO6
Week 08 Spatial Audio and Stereo Decomposition Block teaching (5 hr) LO1 LO2 LO6
Week 09 Spatial Audio and Stereo Decomposition Block teaching (5 hr) LO1 LO2 LO6
Week 10 Microphone Arrays and Dereverberation Block teaching (5 hr) LO1 LO2 LO6
Week 11 Deep Networks and Speech Block teaching (5 hr) LO1 LO2 LO6
Week 12 Deep Networks and Speech, Compressed Sensing and/or Catch-Up Block teaching (5 hr) LO1 LO2 LO6
Week 13 Review Block teaching (5 hr) LO1 LO2 LO6

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 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. 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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.