[3N1-IS-2d] Machine learning (4/5)

[3N1-IS-2d-04] MIDI note embedding with fastText model

〇Yingfeng Fu¹, Yusuke Tanimura^2,1, Hidemoto Nakada^2,1 (1. University of Tsukuba, 2. AIST)

Keywords:FastText, Word embedding, Music information retrieval

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.