Distributed Vector Representations of Folksong Motifs

03/20/2019

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by   Aitor Arronte-Alvarez, et al.

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Universidad Politécnica de Madrid

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University of Hawaii

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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.