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Abstract
Several pointset patterndiscovery and compression algorithms designed for analysing music are reviewed and evaluated. Each algorithm takes as input a pointset representation of a score in which each note is represented as a point in pitchtime space. Each algorithm computes the maximal translatable patterns (MTPs) in this input and the translational equivalence classes (TECs) of these MTPs, where each TEC contains all the occurrences of a given MTP. Each TEC is encoded as a ⟨pattern,vector set⟩ pair, in which the vector set gives all the vectors by which the pattern can be translated in pitchtime space to give other patterns in the input dataset. Encoding TECs in this way leads, in general, to compression, since each occurrence of a pattern within a TEC (apart from one) is encoded by a single vector, that has the same information content as one point. The algorithms reviewed here adopt different strategies aimed at selecting a set of MTP TECs that collectively cover (or almost cover) the input dataset in a way that maximizes compression. The algorithms are evaluated on two musicological tasks: classifying folk song melodies into tune families and discovering repeated themes and sections in pieces of classical music. On the first task, the bestperforming algorithms achieved success rates of around 84%. In the second task, the best algorithms achieved mean F1 scores of around 0.49, with scores for individual pieces rising as high as 0.71.
Original language  English 

Title of host publication  Computational Music Analysis 
Editors  David Meredith 
Number of pages  32 
Volume  Part V 
Place of Publication  Cham, Switzerland 
Publisher  Springer 
Publication date  2016 
Edition  1 
Pages  335366 
Chapter  13 
ISBN (Print)  9783319259291 
ISBN (Electronic)  9783319259314 
DOIs  
Publication status  Published  2016 
Keywords
 geometric pattern discovery
 music analysis
 compression
 algorithms
 pointset patterns
 pattern mining
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Projects
 1 Finished

Lrn2Cre8: Learning to Create
EU Seventh Framework Programme (FP7)
01/10/2013 → 30/09/2016
Project: Research