Scalar context in musical models

Abstract

In this paper, we examine relationships between signature transformations, Filtered Point-Symmetry (FiPS), and voice-leading spaces, with a strong emphasis on the role of scalar context in each model. While these models overlap, the differences between them are of substantial analytical importance. We determine how to expand the signature group using FiPS, and map familiar transformational graphs to maximally even coordinate spaces, thereby separating transformational groups from the scalar contexts in which they are most often explained. We also look at differences in the definition and usage of maximal evenness in continuous space, and examine the impact of scalar context on voice-leading distance.

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