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Abstract
Motivic analysis provides very detailed understanding of musical composi- tions, but is also particularly difficult to formalize and systematize. A computational automation of the discovery of motivic patterns cannot be reduced to a mere extraction of all possible sequences of descriptions. The systematic approach inexorably leads to a proliferation of redundant structures that needs to be addressed properly. Global filtering techniques cause a drastic elimination of interesting structures that damages the quality of the analysis. On the other hand, a selection of closed patterns allows for lossless compression. The structural complexity resulting from successive repetitions of patterns can be controlled through a simple modelling of cycles. Generally, motivic patterns cannot always be defined solely as sequences of descriptions in a fixed set of dimensions: throughout the descriptions of the successive notes and intervals, various sets of musical parameters may be invoked. In this chapter, a method is presented that allows for these heterogeneous patterns to be discovered. Motivic repetition with local ornamentation is detected by reconstructing, on top of “surface-level” monodic voices, longer-term relations between non-adjacent notes related to deeper structures, and by tracking motives on the resulting syntagmatic network. These principles are integrated into a computational framework, the MiningSuite, developed in Matlab.
Original language | English |
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Title of host publication | Computational Music Analysis |
Editors | David Meredith |
Number of pages | 30 |
Volume | Part V |
Place of Publication | Cham, Switzerland |
Publisher | Springer |
Publication date | 2016 |
Edition | 1 |
Pages | 273-302 |
Chapter | 11 |
ISBN (Print) | 978-3-319-25929-1 |
ISBN (Electronic) | 978-3-319-25931-4 |
DOIs | |
Publication status | Published - 2016 |
Keywords
- motivic analysis
- pattern mining
- closed pattern
- cyclic pattern
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Dive into the research topics of 'Automated Motivic Analysis: An Exhaustive Approach Based on Closed and Cyclic Pattern Mining in Multidimensional Parametric Spaces'. Together they form a unique fingerprint.Projects
- 1 Finished
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Lrn2Cre8: Learning to Create
Meredith, D. & Bemman, B.
EU Seventh Framework Programme (FP7)
01/10/2013 → 30/09/2016
Project: Research