The goal of this project is to gain a better understanding of dance music composition in the long 19th-century so as to improve our historical understanding of these styles and to improve our understanding of the knowledge structures and learning processes that go into composing dance and other musics. These are united in our further goal of creating learning resources so that composers can learn to compose in these dance styles and incorporate them in other music. The relation between human learning and knowledge structures and music machine learning and AI music generation will also be considered.
At its most fundamental level the project looks at what the individual human knowledge structures must be in order for someone to compose a piece of music in a particular genre given the inherent universal limitations of human cognition.
The first phase of this project was to develop the learning science framework in relation to dance music composition and the analytical methodology necessary to examine the chosen musics.
The second stage is early analysis and presenting initial analytical findings and the publication of previsionary classification systems.
The third stage is analysis and publication of limited case studies and a refinement of the analytical systems.
The fourth stage is open ended. The amount of music to be fruitfully analysed is immense. Because of this the aim is a continual publication of style analysis and corresponding composition learning guides. Our understanding of the knowledge structures that go into composing music and their function and development will also be continually enhanced through each style analysed.
I will link all publications related to this project here so feel free to bookmark this page to easily check in with the latest composing guides and analytical publications.
