Creating perceptual experts in Australia's policing and security agencies. This project aims to create the next generation of experts in Australia’s policing and national security agencies, by improving crime scene evidence interpretation. Agencies are under pressure to develop more rigorous training practices that go beyond mere intuition and tradition. This project will use a novel approach that directs learning toward the most diagnostic perceptual cues. Expected outcomes include a solid empi ....Creating perceptual experts in Australia's policing and security agencies. This project aims to create the next generation of experts in Australia’s policing and national security agencies, by improving crime scene evidence interpretation. Agencies are under pressure to develop more rigorous training practices that go beyond mere intuition and tradition. This project will use a novel approach that directs learning toward the most diagnostic perceptual cues. Expected outcomes include a solid empirical basis for national training programs designed to create experts that are accurate, reliable, and continuously improving. Improving the training of experts will ensure the integrity of forensics as evidentiary tools available to police, lead to more reliable courtroom convictions and help safeguard Australia from terrorism and crime.Read moreRead less
Music can speak for you: making music with a deep net partner. This project aims to develop and evaluate a novel computational partner to aid composers and non-musicians to make personal music. One computational component learns to output musical structures that another component moulds towards user-desired features while encouraging innovation and exploration. Listeners’ evaluation of the musical outputs in terms of affect will be analysed, potentially allowing us to extend current music genera ....Music can speak for you: making music with a deep net partner. This project aims to develop and evaluate a novel computational partner to aid composers and non-musicians to make personal music. One computational component learns to output musical structures that another component moulds towards user-desired features while encouraging innovation and exploration. Listeners’ evaluation of the musical outputs in terms of affect will be analysed, potentially allowing us to extend current music generation software considerably. The expected outcomes will be a tool for musicians, but also for untrained people, young and older, allowing such untrained people to make personalized music. The tool can thus provide benefits to the creative arts, and to the educational and wellbeing support sectors.Read moreRead less