  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.
