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Distributed Vector Representations of Folksong Motifs

  • Aitor Arronte Alvarez
  • Francisco Gómez-MartinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11502)

Abstract

This article presents a distributed vector representation model for learning folksong motifs. A skip-gram version of word2vec with negative sampling is used to represent high quality embeddings. Motifs from the Essen Folksong collection are compared based on their cosine similarity. A new evaluation method for testing the quality of the embeddings based on a melodic similarity task is presented to show how the vector space can represent complex contextual features, and how it can be utilized for the study of folksong variation.

Keywords

Folksong motifs Melodic context Motif embedding Word2vec 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Aitor Arronte Alvarez
    • 1
  • Francisco Gómez-Martin
    • 2
    Email author
  1. 1.Center for Language and TechnologyUniversity of Hawaii at ManoaHonoluluUSA
  2. 2.Applied Mathematics DepartmentTechnical University of MadridMadridSpain

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