DESPITE THE CONSIDERATION THAT musical parallelism is

an important factor for musical segmentation, there have been relatively few systematic attempts to describe exactly how it affects grouping processes. The main problem is that musical parallelism itself is difficult to formalize. In this study, a computational model that extracts melodic patterns from a given melodic surface is presented. Following the assumption that the begin-ning 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 prima-rily in terms of frequency of occurrence and length of pattern. The special status of nonoverlapping, immedi-ately repeating patterns is examined. All the discovered patterns merge into a single “pattern ” segmentation profile that signifies points in the surface most likely to be perceived as points of segmentation. The effec-tiveness of the proposed melodic representations and algorithms is tested against a series of melodic surfaces illustrating both strengths and weaknesses of the approach