Article
| Title: | Melodic segmentation: structure, cognition, algorithms |
| Author: | |
| Source document: | Musicologica Brunensia. 2017, vol. 52, iss. 1, pp. 53-61 |
| Extent: | 53-61 |
| ISSN: | 1212-0391 (print); 2336-436X (online) |
| DOI: | 10.5817/MB2017-1-5 |
| Type: | Article (English) |
| Stable URL: | http://hdl.handle.net/11222.digilib/136927 |
| License: |
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Abstract(s):
Segmentation of melodies into smaller units (phrases, themes, motifs, etc.) is an important process in both music analysis and music cognition. Also, segmentation is a necessary preprocessing step for various tasks in music information retrieval. Several algorithms for automatic segmentation have been proposed, based on different music-theoretical backgrounds and computing approaches. Rule-based models operate on a given set of logical conditions. Learning-based models, originating in linguistics, compute segmentation criteria on the basis of statistical parameters of a training corpus and/or of the given composition. The aim of this preliminary study is to propose and describe a new segmentation algorithm that is rule-based, parsimonious, and unambiguous.
Keywords:
segmentation; melody; phrase; motif; music information retrieval
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