Distributed Vector Representations of Folksong Motifs

2019/03/20 by Aitor Arronte-Alvarez, Arronte-Alvarez, Aitor, Francisco Gómez +1
Computer Science · #Audio and Speech Processing (eess.AS) #Computation and Language (cs.CL) #FOS: Computer and information sciences #FOS: Electrical engineering #H.5.5 #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Music and Audio Processing #Natural Language Processing Techniques #Sound (cs.SD) #Topic Modeling #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1903.08756

openalex publication_date 2019/03/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
• Distributional Structure

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