MIDI note embedding with fastText model

Abstract

Distributed word representation greatly promoted research in NLP. Same as languages, MIDI music is constructed in the way of sequence, with a determined alphabet of notes and events. We proposed a way of training MIDI note embedding with an adaption of Facebook's fastText model. We then evaluate the model by word similarity, word analogy, and a classification task. The result shows that the adopted fastText model generalizes well in MIDI data and it’s promising to be used on future downstream tasks.