Music is an art form with a very long history, and continues to engage millions of people today. Music Information Retrieval (MIR), the exciting interdisciplinary science that brings together music and computer science, is a growing field of research with the potential to enrich pure computer science knowledge while creating real-world applications that the general public can benefit from. While the marriage of art and science is often troublesome, MIR has the benefit that many aspects of music are highly structural and have been subject to rigorous formalisation for a long time. Formalisation and computers go hand in hand, and MIR researchers have therefore been developing models of musical structure for many years, and putting them to use in several applications. However, such models, so far, have had limited impact; they are commonly restricted to one specific aspect of music (such as harmony or form), can be hard to implement computationally (due, for example, to the way ambiguity is handled), and are often too technical to be used directly by musicologists who are not familiar with programming language details.
However, models are valuable. Unlike machine learning approaches, model-based MIR provides a real insight about the underlying structure, and can benefit from the input of musicologist experts. Furthermore, a single model can be applied to multiple important MIR tasks (such as retrieval, analysis, and automatic composition). The research goal of this project is thus to give musical models the impact they deserve, advancing the practical embodiment of hierarchical musical structure—in its various forms—in computer science through the development of new, functional Models of Structure in Music (MoStMusic). Specifically, I intend to develop functional models of musical form, melody, and harmony that enable an easy, fast, and flexible way of creating model-enhanced MIR applications. Being executable, these models will pave the way for true content-based music analysis and retrieval---an underestimated and underexplored area. As a showcase of a model-enhanced application, I will create an online music analyser that automatically computes the structure present in a user-submitted piece, and displays it in an interactive interface that highlights the structural shape of music.