Year 2007 Final-Year Projects in
SIGNALS AND
INFORMATION PROCESSING SYSTEMS
Dr.
Roberto Togneri
We are currently in the first century of the information age and the
new era of information systems engineering. The Signals and Information
Processing (SIP) Group is offering exciting and challenging final-year
projects to students who can demonstrate the required interest,
motivation and passion. If you are interested in any of the projects on
offer (or you have suggestions of your own) please email Roberto Togneri at
<roberto[@]ee[.]uwa[.]edu[.]au> or drop by Room 4.10 for
an obligation free discussion, additional information and even reading material to help you make a truly informed choice.
Slides used for SIP Group Project Presentation on Tuesday, September 5
PLEASE NOTE: Most
of the projects will require proficiency either in
the MATLAB
programming language, shell scripting and/or UNIX command-line
environments. Most students
should be familiar with MATLAB, operate comfortably in a UNIX
environment and be able to learn the basics of shell scripting if required. This
should take you no more than 1-2 weeks practice depending
on your current programming and computing skills. You may like to refer
to my online documentation and tutorials on MATLAB and UNIX/Shell
to help you with this. For projects emphasising real-time or embedded implementations proficiency in C/C++
Programming will be important.
STOP! Some important questions you should ask yourself:
- Do you have a course weighted average of 78 or better?
- Are you looking at getting a first class honours?
- Are you still considering all options available to you after you graduate?
If you answered yes to the above, then you should be seriously considering post-graduate studies after you graduate in 2007.
For more information please see:
First
... most of the projects I offered to students this year are still
available to interested students for next year, please check my 2006 FYP page for the the details of these projects.
Now ... to the exciting Final-Year Projects being offered for students next year:
4.A. Secure Command and Control using Speech Recognition and Speaker Verification
This is a systems engineering project where you will build a prototype speech recognition system (see http://www.cslu.ogi.edu/HLTsurvey/ch1node4.html) and/or speaker verification system (see http://www.cslu.ogi.edu/HLTsurvey/ch1node9.html) using the Hidden Markov Model Toolkit (HTK)
software (see http://htk.eng.cam.ac.uk). Your application domain is a
futuristic automated home where voice and sound is used to respond to
commands and authenticate occupants. Possible tasks include:
sound-activated light switch, voice-controlled TV remote, security user
authentication by voice, voice-dialling, etc. This project can be
as simple as you like (speaker-dependent, isolated word recognition in
quiet environments) to as challenging and exciting as you like
(continuous speech recognition in noisy environments) and investigating
different aspects (voice activity detection, keyword spotting, spoken
language understanding, etc.). Note that more than one student can work on this project.
4.B. Reconstruction of Noise Corrupted Speech Spectrograms
Speech signals are usually transformed into the time-frequency domain
and represented as spectrographic images (spectrograms, see http://en.wikipedia.org/wiki/Spectrogram)
where the two axes of the image represent time and frequency
respectively. The pixel value of each element in the image marks the
energy of the signal in that time-frequency location. Different regions
of this spectrographic picture may be corrupted to different degrees by
noise. The purpose of this project is the investigation of methods to
reconstruct the damaged regions of spectrograms prior to recognition
from the information available in reliable regions and a priori
knowledge about the structure of speech. Once the reconstruction
process is completed standard speech recognition methods and feature
extraction techniques can be applied. The HTK toolkit will be used for
the speech recognition experiments and MATLAB for implementing the
reconstruction algorithms. Note that this project will be co-supervised with a SIP Lab PhD student.
4.C. Single Channel Blind Source Separation
The ability to separate different sources of sound is an important
mechanism for speech enhancement and robust speech recognition.
Consider an environment with the speaker and background music, or the
speaker with background car noise, etc.. With microphone array
processing multiple channels allow spatial filtering to help isolate
the speaker. But there are many applications where only a single
channel is available. In this challenging but satisfying project you
will investigate and implement a technique based on independent
component analysis (ICA) to identify and isolate the different sources
of sound in a single channel recording. You will need to both
understand the basics of the theory, implement the algorithms in
MATLAB, and carry out the investigations on synthetically designed
audio samples (e.g. separating speech from music).
4.D. Performance Evaluation of Auditory Models
Computational auditory models replicate the process of human perception
of speech and sound in the peripheral auditory system. They have been
used as a tool for speech processing, speech and voice analysis as well
as in the investigation of auditory phenomena. Two software tools are
available for auditory modeling: Auditory Image Modeling (AIM) (see http://www.pdn.cam.ac.uk/groups/cnbh/research/aim.html) and Development System for Auditory Modeling (DSAM) (see
http://www.essex.ac.uk/psychology/hearinglab/dsam).
AIM is intended to simulate the processing performed by the auditory
system to convert a sound into your first conscious awareness of that
sound, that is your auditory image of the sound. The processing steps
are pre-cochlear processing, basilar membrane motion, the neural
activity pattern and construction of the auditory image. DSAM brings
together established models which simulate various stages in the
auditory process. The DSAM
library is a computational platform and set of coding conventions which
supports a modular approach to auditory modeling. Available with
DSAM is the Auditory Model Simulator (AMS) application which is a
fully-fledged, ready to use application with a graphical user
interface. It comes as a finished product for Windows and Linux
platforms with ready-complied installation package. Also available is
the "AutoTest" for testing DSAM routines. Both packages are written in
C and also have
a MATLAB version (AIM-MAT and AFM).
The project involves implementation of computer models of auditory
systems using the existing development tools for auditory modeling. The
student will implement different established models including some of
the models that have been developed here at the SIP Lab. The main task
will be to evaluate the relative performance and efficiency of these
models in identifying perceptual properties of speech. Note that this project will be co-supervised with a SIP Lab PhD student.
4.E. Music Classification and Summarization
With the increasing stock of available music files which are ripped or
downloaded to portable music players, MP3 CDs, etc. it becomes
increasingly important to be able to more efficiently search and index
music in cases where the title or artist is unknown. In this project
you will perform simple classification of the different music genres
(rock, jazz, ambient, classic) and sub-genres (rock: pop, metal,
r&b, dance, etc.) using standard pattern classification methods
(see http://cnx.org/content/m11691/latest/).
For a more challenging project you can also consider music segmentation
and summarisation by attempting to detect the key segments of a music
track and identify the "hook" (e.g. chorus) used to summarise the music
piece.
4.F. Nonlinear Function Mapping using the MLP and RBF Neural Networks
Function mapping is the ability to be able to uniquely map data from
one domain to another domain. With parametric mappings an analytic
function, y=f(x), will map points x to y. Parametric, linear mappings
are usually easy to estimate, however nonlinear mappings are much more
complicated. And when the functional form of the mapping is unknown a
parametric mapping is not even possible. In this project you will
examine nonlinear, data-driven mappings using neural networks as
function approximators. Specifically you will use the MLP and RBF
networks to determine a mapping between the speech vocal tract
resonances (or formants) and the speech acoustic spectral features. You
can either perform acoustic-to-formant mapping (a formant tracker) or
the more difficult formant-to-acoustic mapping. You will carry out your
investigations under MATLAB and generate the required formant and
acoustic data using the various speech software tools and corpora. Note that your results will make an important contribution to the next generation dynamic acoustic models for speech.
4.G. Real-time EEG processing for interactive ERP and TMS
Interactive ERP/TMS is
a new process in electrophysiology whereby stimuli are delivered in
response to selected short term patterns of the electroencephalogram
(EEG). For event related potential research, stimuli may be auditory,
visual or somatosensory stimuli, while for TMS work, the stimuli are
high-powered magnetic pulses. Interactive recordings are used in basic
research of cognition, in epidemiological research (schizophrenia) and
in clinical trials in the treatment of depression and schizophrenia.
They are also being tested brain computer interface (BCI) applications
whereby devices are controlled directly by "thought" activity. Highly
motivated students would be expected to select and implement their own
processing paradigm, algorithm, and software for evaluation. Processing
pattern recognition methods that have previously been used in
interactive recording include syntactic analysis, spectral analysis and
phase or amplitude thresholds. The only requirement being to
recognise a brainwave state in sufficient time to deliver a stimulus
before the state changes. Training on the technical and clinical
aspects of EEG/ERP will be provided at CCRN (http://www.ccrn.uwa.edu.au),
along with a scored corpus of EEG data to allow (offline) retrospective
development/training of such a system. CCRN then provides TMS and ERP
stimuli delivery systems, subject assistance and clinical supervision
to potentially implement the system (online) as a possible new
treatment. Note that is a collaborative project between the SIP Group and CCRN.
4.H. Etch Pit Density (EPD) of Semiconductor Wafers
The Etch
Pit Density (EPD) of semiconductor wafers is a measure of the number of
defects and its accurate calculation is important. The defects are
visibly enhanced when the wafer is wet etched in a certain chemical
solution. An image of the wafer surface reveals the pit defects as
small spots. The defects shown by the image need to be detected,
classified as defects and then counted. This process is not unlike
particle counting used in microbiology. In the project a highly
motivated student with a sound background in image processing,
programming and signal processing will be required to investigate and
implement strategies for enhancing the defects by appropriate image
processing (e.g. thresholding, etc.), detect and classify the defects
by recognising the defect characteristics and counting the number of
defects. Part of this investigation can include evaluating the ImageJ
public-domain software (see http://rsb.info.nih.gov/ij) for this purpose. Note that this project will be jointly supervised with the Microelectronic Research Group (MRG).
4.I. Performance Analysis of Cryptographic Algorithms
In the modern digital information age there is a widespread need for
effective cryptography. The use of encryption is common for secure
transactions, secure storage of sensitive, user authentication and
digital rights management. There are many available cryptographic
algorithms that one can use for both private-key and public-key
encryption (see http://www.ssh.fi/support/cryptography and http://www.eskimo.com/~weidai/algorithms.html).
In this project a highly motivated student with the right Maths or CS
background can choose to investigate different aspects of cryptographic
algorithm performance including: implementation of more efficient
Elliptic Curve (EC) public-key encryption and comparison with RSA or
the seriousness of timing attacks based on cache, CPU and memory
profiling by evaluations and solutions. Alternatively for a more
straightforward project you can benchmark the performance of various
public-domain algorithms (see http://www.eskimo.com/~weidai/benchmarks.html). Note that this project is supported by the interests of Motorola Research, Australia.
4.J. Evaluation of an Identity-Based Encryption Scheme
The biggest drawback of standard public key
infrastructure (PKI), aside from simply getting people to use it, is
the rigmarole of generating and distributing public keys. One potential
solution was proposed nearly 20 years ago. It's called Identity-Based
Encryption (see http://crypto.stanford.edu/ibe/).
With IBE the sender can encrypt the text using a human-readable string
as the key (e.g. the recipients email address). At the other end the
recipient then has to retrieve or generate the corresponding private
key to decrypt the ciphertext. In this project you will investigate,
implement and evaluate a simple IBE prototype scheme and compare its
performance, useability and applicability to an equivalent PKI scheme.
Students for this project should have either have a CS or Maths major
background. Note that this project is supported by the interests of Motorola Research, Australia.
Want more? Have a look at my 2008 FYP
list for the projects which didn't make it this time round and which
may be offered to students for 2008, but are available to students in
2007 if you are interested.
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