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Constructing proximity graphs to explore similarities in large-scale melodic datasets

Constructing proximity graphs to explore similarities in large-scale melodic datasets

Walshaw, Chris ORCID: 0000-0003-0253-7779 (2016) Constructing proximity graphs to explore similarities in large-scale melodic datasets. In: 6th International Workshop in Folk Music Analysis. Dublin Institute of Technology, pp. 22-29.

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Abstract

This paper investigates the construction of proximity graphs in order to allow users to explore similarities in melodic datasets. A key part of this investigation is the use of a multilevel framework for measuring similarity in symbolic musical representations. The basis of the framework is straightforward: initially each tune is normalised and then recursively coarsened, typically by removing weaker off-beats, until the tune is reduced to a skeleton representation with just one note per bar. Melodic matching can then take place at every level: the multilevel matching implemented here uses recursive variants of local alignment algorithms, but in principle a variety of similarity measures could be used. The multilevel framework is also exploited with the use of early termination heuristics at coarser levels, both to reduce computational complexity and, potentially, to enhance the matching qualitatively. The results of the matching algorithm are then used to construct proximity graphs which are displayed as part of an online interface for users to explore melodic similarities within a corpus of tunes.

Item Type: Conference Proceedings
Title of Proceedings: 6th International Workshop in Folk Music Analysis
Additional Information: Workshop held from 15-17 June 2016 at the Dublin Institute of Technology new campus in Grangegorman.
Uncontrolled Keywords: Multilevel refinement; Melodic similarity
Subjects: M Music and Books on Music > MT Musical instruction and study
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Department of Computing & Information Systems
Last Modified: 26 Sep 2017 09:47
Selected for GREAT 2016: None
Selected for GREAT 2017: GREAT a
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/15593

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