ARTIFICIAL INTELLIGENCE, MELODY AND EDUCATION
Matt Smith, Simon Holland
Smith’s constraint-based learning tool MOTIVE for exploring melody works within a constraint-based methodology. The aim of MOTIVE is to support beginners to explore the composition of melody. The work achieves potentially very general applicability to melody, irrespective of genre, by virtue of being based on the most fundamental psychologically grounded theory of melody currently available Narmour’s (1989) cognitive theory of melody.
Narmour’s theory has known problems and limitations (Cumming, 1992), but has little competition as a theory of melody framed substantially in psychological terms. Narmour’s (1989) analytical theory of tonal melody uses simple extensions to low level gestalt processing theory for melodic notes to predict how a listener will break a melody up into groups of contiguous notes, and which notes will be perceived as more important than others (other things being equal). This gives rise to hierarchical parse trees which recursively reduce the melody to simpler versions, roughly analogous to Lerdahl and Jackendoff (1983) TSR trees.
A central contribution of Smith’s work is that, in order to be able to make use of Narmour’s theory computationally, an explicit, consistent computational model of the theory had to be refined and implemented. Having done this, Smith was able to test Narmour’s published hand-produced analyses for consistency against the computational version. The tests showed the theory to be internally coherent, with some gaps to be filled in, but with no fatal internal flaws. This computational model then became the central component of Smith’s teaching system, MOTIVE. MOTIVE uses a constraint based planner to parse melodies. MOTIVE is able to ‘replan’ or recompose melodies by navigating trees of related melodies, while holding constant, or varying structural features of the melody at any level. Thus an ‘analysis by recomposition’ strategy can be applied, as well as other teaching strategies.
Apart from possible impacts in education, it is highly likely that Smith’s work will serve as a useful computational tool to help explore, modify, or refine Narmour’s theory.
M. Smith & S. Holland, (1994) “MOTIVE: A constraint-based tool for melody analysis and generation”, In M. Smith, A. Smaill & G. Wiggins (eds), “Music Education: An Artificial Intelligence Approach”, Springer-Verlag
M. Smith & S. Holland, (1993) “MOTIVE: A constraint-based tool for melody analysis and generation”, Proceedings of Workshop on Artificial Intelligence, Music and Education, part of AIED-93, Edinburgh, Scotland
M. Smith & S. Holland, (1992) “An AI tool for the Analysis and Generation of Melodies”, Proceedings of International Computer Music Conference, San Jose, California, USA
M. Smith & S. Holland, (1992) “An Intelligent Tutor for Melody Composition”, Proceedings of International Conference on Systems Research, Informatics and Cybernetics, Baden-Baden, Germany