  A representation technique that allows encoding music in a way that contains
musical meaning would improve the results of any model trained for computer
music tasks like generation of melodies and harmonies of better quality. The
field of natural language processing has done a lot of work in finding a way to
capture the semantic meaning of words and sentences, and word embeddings have
successfully shown the capabilities for such a task. In this paper, we
experiment with embeddings to represent musical notes from 3 different
variations of a dataset and analyze if the model can capture useful musical
patterns. To do this, the resulting embeddings are visualized in projections
using the t-SNE technique.
