To map and enhance Australian musical improvisation as a creative industry. The project maps transforming improviser networks in Australian music since 1970, to inform how cultural innovation develops and disseminates. Application of new statistical techniques (temporal network analysis) will combine with in-depth focus groups to show how improvisation excellence depends on a mix of artistic craft, networked collaboration and institutional support. This knowledge will assist music venues and ind ....To map and enhance Australian musical improvisation as a creative industry. The project maps transforming improviser networks in Australian music since 1970, to inform how cultural innovation develops and disseminates. Application of new statistical techniques (temporal network analysis) will combine with in-depth focus groups to show how improvisation excellence depends on a mix of artistic craft, networked collaboration and institutional support. This knowledge will assist music venues and industry in nurturing improvisation as a cultural force and commercial opportunity for export and tourism attraction post Covid-19. The novel method, integrating computational network analysis with qualitative research, will also inform and build capacity for future understandings of cultural fields and industries.Read moreRead less
Developing a personalised Music Affect Recommender System. The project aims to develop a personalised music recommender system using perceived tone quality, affect and liking. Recommender systems using prior verbal annotations and ratings are common (Amazon) but inappropriate for less popular music by unfamiliar artists, which lacks social use data. The project intends to build on work into perception of musical affect and its relation to loudness and tone quality; and the automation of the orga ....Developing a personalised Music Affect Recommender System. The project aims to develop a personalised music recommender system using perceived tone quality, affect and liking. Recommender systems using prior verbal annotations and ratings are common (Amazon) but inappropriate for less popular music by unfamiliar artists, which lacks social use data. The project intends to build on work into perception of musical affect and its relation to loudness and tone quality; and the automation of the organisation of digital libraries both by labels and acoustic content. Developing this, the project seeks to create a model that gives recommendations which accounts for an individual's preferences based on acoustic content, affect and liking. The system will be designed to update rapidly and to encourage exploration of familiar and unfamiliar music.Read moreRead less