Determinants of Audio-Visual effects in degraded and non-degraded speech. Seeing a speaker's face can affect the perception of their speech in a number of ways. This project proposes a detailed comparison of factors that affect Audio-Visual (AV) facilitation of degraded speech detection and identification. Detection-based tasks should be more sensitive to signal based correlations whereas identification-based effects more sensitive to complementary information. The significance of the current pr ....Determinants of Audio-Visual effects in degraded and non-degraded speech. Seeing a speaker's face can affect the perception of their speech in a number of ways. This project proposes a detailed comparison of factors that affect Audio-Visual (AV) facilitation of degraded speech detection and identification. Detection-based tasks should be more sensitive to signal based correlations whereas identification-based effects more sensitive to complementary information. The significance of the current proposal is that it offers both a strategy and a connected series of experiments for determining key behavioural constraints on AV speech integration. Understanding AV interactions will build links between neurophysiological processes and coherent perception and have important implications for AV application.Read moreRead less
Adaptive learning of spatiotemporal patterns: Development of multi-layer spiking neuron networks using Hebbian and competitive learning. The aim of this project is to develop a method for recognising patterns that change in time. The development of a reliable method that is fast and robust to noise will have wide application in many areas, especially computer speech recognition where timing plays a crucial role. Building-blocks similar to those in the brain (spiking neurons) will be used. Aut ....Adaptive learning of spatiotemporal patterns: Development of multi-layer spiking neuron networks using Hebbian and competitive learning. The aim of this project is to develop a method for recognising patterns that change in time. The development of a reliable method that is fast and robust to noise will have wide application in many areas, especially computer speech recognition where timing plays a crucial role. Building-blocks similar to those in the brain (spiking neurons) will be used. Automatic techniques will be used to teach groups of spiking neurons the differences between sequences of events by adjusting connections between them. The significance of this approach is that it captures information about timing that is missed in existing techniques.Read moreRead less
Adaptive learning in networks of spiking neurons for recognising patterns that change with time. The aim of this project is to develop a method for recognising patterns that change with time. Building-blocks similar to those in the brain (spiking neurons) will be used. Automatic techniques will be developed to teach groups of spiking neurons the differences between sequences of events by adjusting connections between neurons. The significance of this approach is that it captures information abou ....Adaptive learning in networks of spiking neurons for recognising patterns that change with time. The aim of this project is to develop a method for recognising patterns that change with time. Building-blocks similar to those in the brain (spiking neurons) will be used. Automatic techniques will be developed to teach groups of spiking neurons the differences between sequences of events by adjusting connections between neurons. The significance of this approach is that it captures information about timing that is missed in existing techniques. The development of a reliable method that is fast and robust to noise will have wide application in many areas, especially computer speech recognition where timing plays a crucial role.Read moreRead less
Temporal Pattern Learning and Recognition in Neural Systems. This project is relevant to the National Research Priority area of Frontier Technologies and addresses fundamental cross-disciplinary issues of how neural systems learn patterns that change with time, which is at the cutting edge of intelligent processing systems. Applications are in rapidly growing fields of automatic speech processing, robotics, machine learning and intelligent systems, all with applications in areas of economic impo ....Temporal Pattern Learning and Recognition in Neural Systems. This project is relevant to the National Research Priority area of Frontier Technologies and addresses fundamental cross-disciplinary issues of how neural systems learn patterns that change with time, which is at the cutting edge of intelligent processing systems. Applications are in rapidly growing fields of automatic speech processing, robotics, machine learning and intelligent systems, all with applications in areas of economic importance. Application to cochlear implant speech processing will provide benefit for the hearing impaired. The project will provide students with training at an international level within Australia, thus helping ensure Australia maintains and extends its science and technology base into the future.Read moreRead less
Spatial Learning And Memory In Huntington's Disease
Funder
National Health and Medical Research Council
Funding Amount
$475,969.00
Summary
This project will develop a spatial learning and memory test battery sensitive to dementia in Huntington’s disease, relate the task to atrophy in key brain regions, and then apply the test in a clinical trial aimed at developing a regeneration of damaged brain regions in Huntington’s disease. The overarching goal is to develop a cognitive test that is closely aligned to brain pathology in dementia as a tool for more precise, mechanism-based investigations in the dementia clinical trial setting.
Using Diffusion MRI For Understanding The Relationship Between Memory Decline And Corticothalamic Tracts
Funder
National Health and Medical Research Council
Funding Amount
$57,578.00
Summary
Stroke populations are at a risk of dementia. Structural changes have been demonstrated to precede cognitive changes, providing a potential for early diagnosis and intervention. Magnetic resonance imaging markers of structural connectivity are powerful predictors of dementia. As a longitudinal study, this proposal has the unique advantage that I will be able to detect changes in post-stroke brain networks in the 3 years after stroke. This raises the potential for future clinical application.
Discovery Early Career Researcher Award - Grant ID: DE170100106
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Measuring interference from prior memories using experience sampling. The project aims to better understand the causes of forgetting in recognition memory. This project will measure participants' experiences using smartphone technology for four weeks before a recognition memory experiment. Similarities between the images in the experiment and images in prior experience can be used to fully specify all interference components within a computational model of recognition memory, leading to a comple ....Measuring interference from prior memories using experience sampling. The project aims to better understand the causes of forgetting in recognition memory. This project will measure participants' experiences using smartphone technology for four weeks before a recognition memory experiment. Similarities between the images in the experiment and images in prior experience can be used to fully specify all interference components within a computational model of recognition memory, leading to a complete model of recognition memory. Better understanding the causes of forgetting in recognition memory could show how interference contributes to memory impairments in ageing, and ultimately Alzheimer’s and other clinical disorders.Read moreRead less
Understanding Universal Immunity To Influenza Viruses
Funder
National Health and Medical Research Council
Funding Amount
$687,975.00
Summary
A/Prof Kedzierska’s work combines cutting-edge basic research with unique clinical studies to define how to generate protective immunity against the pandemic and newly emerged influenza viruses. This research will identify key factors that drive the severe and fatal influenza disease in high-risk groups, including the young, elderly, pregnant women and Indigenous Austraians. Findings on the optimal human immunity to influenza viruses will be applicable to other infectious diseases and cancers.
Robust face detection and recognition for computer-based security surveillance. The research aims at improving the existing and creating new automated face detection and recognition methods by making them invariant, firstly to head pose, orientation, scale and rotation, and then to occlusion, lighting conditions and facial expressions.
A robust face detector will be developed first and then a new face recognition algorithm that continues to learn identity-specific discriminants on-line by co ....Robust face detection and recognition for computer-based security surveillance. The research aims at improving the existing and creating new automated face detection and recognition methods by making them invariant, firstly to head pose, orientation, scale and rotation, and then to occlusion, lighting conditions and facial expressions.
A robust face detector will be developed first and then a new face recognition algorithm that continues to learn identity-specific discriminants on-line by collecting incremental face exemplars. The result of the research will be an algorithm that can improve its performance on-line adapting in a stable learning process each identity model to the correct facial examples.
The research has significant practical implication in visual surveillance increasing the robustness of identification of person identity, state and intent.
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Intruder alert! detecting and classifying events in noisy time series. This project aims to address the mathematical challenges in automated early detection and classification of intrusion events in noisy time series generated from perimeter security systems. The project expects to develop robust methods to detect intrusion events under different operating environments while ignoring nuisance events. The project will boost the global competitiveness of the Australian security industry, and enabl ....Intruder alert! detecting and classifying events in noisy time series. This project aims to address the mathematical challenges in automated early detection and classification of intrusion events in noisy time series generated from perimeter security systems. The project expects to develop robust methods to detect intrusion events under different operating environments while ignoring nuisance events. The project will boost the global competitiveness of the Australian security industry, and enable improved event detection and classification in noisy time series to the benefit of many critical application areas beyond national security.Read moreRead less