MOTIVE: The development of an AI tool for beginning melody composers

Smith, Matt and Holland, Simon (1994). MOTIVE: The development of an AI tool for beginning melody composers. In: Smith, Matt; Smaill, Alan and Wiggins, Geraint A. eds. Music Education: An Artificial Intelligence Approach. Workshops in Computing. Springer Verlag, pp. 41–55.

DOI: https://doi.org/10.1007/978-1-4471-3571-5_3

Abstract

The goal of the research described in this paper is to find ways of using artificial intelligence to encourage and facilitate melody composition by musical novices. The first stage of the research is the formalisation of an analytical theory of melody, Eugene Narmour's Implication-Realisation Model. This hypothetical theory offers an explanation of how listeners of music break-up a melody into "chunks", and hear some notes as more important than others. The formalisation process involves the implementation of a declarative parser in Prolog, and then comparison of Narmour's published analyses with those of the parser. With such results it will be possible to present a critical evaluation of Narmour's theory, and the parser.

Around the parser a constraint-generation tool (called MOTIVE) is being built. This paper presents the features of the tool and a possible design for an iconic interface, and we suggest a number of ways in which MOTIVE may facilitate the development of melody composition skills in an educational context.

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