Distributed Vector Representations Of Folksong Motifs
Aitor Arronte-Alvarez, Francisco Gómez-Martin . Lecture Notes in Computer Science 2019 – 5 citations
[Paper]
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.