Signature pedagogies for creative collaboration: Lessons for and from music. This project aims to develop a model of the signature pedagogies and environmental supports that foster the 21st century skills of creativity, innovation, collaboration and cooperation. The project's significance lies in its unique focus on pedagogies of expert creative collaborative practice in four internationally renowned chamber music training environments. These are characterised by individual risk in performance, ....Signature pedagogies for creative collaboration: Lessons for and from music. This project aims to develop a model of the signature pedagogies and environmental supports that foster the 21st century skills of creativity, innovation, collaboration and cooperation. The project's significance lies in its unique focus on pedagogies of expert creative collaborative practice in four internationally renowned chamber music training environments. These are characterised by individual risk in performance, intensified need for collaborative exchange, and the capacity to juxtapose individual accountability within collaborative practices. Expected outcomes and benefits of the project include a model that has translational application and impact for those professions that rely on generating new knowledge in collaborative settings.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