Influence of Musical Similarity on Melodic Segmentation: Representations and Algorithms

In this paper a computational model is presented that extracts patterns from a given melodic surface and, then, following the assumption that the beginning and ending points of 'significant' repeating musical patterns influence the segmentation of a musical surface, the discovered patterns are used as a means to determine probable segmentation points of the melody. ‘Significant’ patterns are defined primarily in terms of frequency of occurrence and pattern length. The special status of non-overlapping immediately repeating patterns is examined. All the discovered patterns merge into a single ‘pattern’ segmentation profile that signifies points in the surface that are most likely to be perceived as points of segmentation. The effectiveness of the proposed melodic representations and algorithms is tested against a series of melodic examples.