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Abstract
Several point-set pattern-discovery and compression algorithms designed for analysing music are reviewed and evaluated. Each algorithm takes as input a point-set representation of a score in which each note is represented as a point in pitch-time 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 pitch-time 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 best-performing 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.
Originalsprog | Engelsk |
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Titel | Computational Music Analysis |
Redaktører | David Meredith |
Antal sider | 32 |
Vol/bind | Part V |
Udgivelsessted | Cham, Switzerland |
Forlag | Springer |
Publikationsdato | 2016 |
Udgave | 1 |
Sider | 335-366 |
Kapitel | 13 |
ISBN (Trykt) | 978-3-319-25929-1 |
ISBN (Elektronisk) | 978-3-319-25931-4 |
DOI | |
Status | Udgivet - 2016 |
Fingeraftryk
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Lrn2Cre8: Learning to Create
EU Seventh Framework Programme (FP7)
01/10/2013 → 30/09/2016
Projekter: Projekt › Forskning