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