Beamforming with acoustic vector sensors for audio user interfaces. We aim to create new Audio User Interfaces (AUIs) for the automatic separation and annotation of audio from complex sound scenes using acoustic vector sensor beamforming technology. Specifically, we will develop: speech AUIs for noisy, multi-talker, reverberant environments; and sound transcription AUIs for the deaf. Ultimately, users will be able to walk into a room, hold conversations and leave with a searchable, automatically ....Beamforming with acoustic vector sensors for audio user interfaces. We aim to create new Audio User Interfaces (AUIs) for the automatic separation and annotation of audio from complex sound scenes using acoustic vector sensor beamforming technology. Specifically, we will develop: speech AUIs for noisy, multi-talker, reverberant environments; and sound transcription AUIs for the deaf. Ultimately, users will be able to walk into a room, hold conversations and leave with a searchable, automatically-generated transcript of the audio events, tagged with metadata. The application of these technologies will create new possibilities for recording audio in the music, radio, TV industries, and future home based audio communication systems.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