Concepts from the theory of sequence comparison are adapted to measure the overall similarity or dissimilarity between two musical scores. A key element is the notion of consolidation and fragmentation, different both from the deletions and insertions familiar in sequence comparison, and from the compressions and expansions of time warping in automatic speech recognition. The measure of comparison is defined so as to detect similarities in melodic line despite gross differences in key, mode or tempo. A dynamic programming algorithm is presented for calculating the measure, and is programmed and applied to a set of variations on a theme by Mozart. Cluster analysis and spatial representation of the results confirm subjective impressions of the patterns of similarities among the variations. A generalization of the Marcel Mongeau obtained his B.Sc. and M.Sc. degrees at the Universitd de Montreal and is currently completing his doctorate at the University of Waterloo. David Sankoff (Ph.D., McGill) is a Professor in the D@artement de mathdmatiques et statistique and is also attached to the Centre de recherches mathdmatiques at the Universitd de Montrdal. His research intersts include sociolinguistics --specifically the quantitative approach inherent in linguistic variation theory --statistical classification theory, biomathematics and computational biology --particularly algorithms for macromolecular sequence analysis and the reconstruction of phylogenetic trees.algorithm is presented for detecting locally similar portions in two scores, and is then applied.
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