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Tune classification using multilevel recursive local alignment algorithms

Tune classification using multilevel recursive local alignment algorithms

Walshaw, Chris ORCID: 0000-0003-0253-7779 (2017) Tune classification using multilevel recursive local alignment algorithms. In: Proceedings of the 7th International Workshop on Folk Music Analysis. Universidad de Malaga, pp. 80-87. ISBN 978-84-697-2303-6

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Abstract

This paper investigates several enhancements to two well-established local alignment algorithms in the context of their use for melodic similarity. It uses the annotated dataset from the well-known Meertens Tune Collection to provide a ground truth and the research aim to answer the question, to what extent do these enhancements improve the quality of the algorithms? In the results, recursive application of the alignment algorithms, applied to a multilevel representation of the melodies, is shown to be very effective for improving the accuracy of the classification of the tunes into families. However, the ideas should be equally applicable to music search and melodic matching.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of the 7th International Workshop on Folk Music Analysis
Additional Information: FMA 2017 - 7th International Workshop on Folk Music Analysis, 14-16 June 2017, Málaga, Spain
Uncontrolled Keywords: Cultural informatics; Music similarity; Melodic classification
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 09:54
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/17512

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