Modeling Musical Context with Word2vec

TitleModeling Musical Context with Word2vec
Publication TypeConference Proceedings
Year of Conference2017
AuthorsHerremans D., Chuan C.-H.
Conference NameFirst International Workshop On Deep Learning and Music
Date Published05/2017
Conference LocationAnchorage, US
Keywordsmusic, music context, neural networks, semantic vector space, word2vec

We present a semantic vector space model for capturing complex polyphonic musical context. A word2vec model based on a skip-gram representation with negative sampling was used to model slices of music from a dataset of Beethoven’s piano sonatas. A visualization of the reduced vector space using t-distributed stochastic neighbor embedding shows that the resulting embedded vector space captures tonal relationships, even without any explicit information about the musical contents of the slices. Secondly, an excerpt of the Moonlight Sonata from Beethoven was altered by replacing slices based on context similarity. The resulting music shows
that the selected slice based on similar word2vec context also has a relatively short tonal distance from the original slice.