Distributed Vector Representations of Folksong Motifs
arXiv:1903.08756
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.
MCM 19
References in corpus (4)
- From Frequency to Meaning: Vector Space Models of Semantics
- Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription
- Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales
- Modeling Musical Context with Word2vec