Impact of time and note duration tokenizations on deep learning symbolic
  music modeling
Nathan Fradet Nicolas Gutowski Fabien Chhel Jean-Pierre Briot
Symbolic music is widely used in various deep learning tasks, including
generation, transcription, synthesis, and Music Information Retrieval (MIR). It
is mostly employed with discrete models like Transformers, which require music
to be tokenized, i.e., formatted into sequences of distinct elements called
tokens. Tokenization can be performed in different ways. As Transformer can
struggle at reasoning, but capture more easily explicit information, it is
important to study how the way the information is represented for such model
impact their performances. In this work, we analyze the common tokenization
methods and experiment with time and note duration representations. We compare
the performances of these two impactful criteria on several tasks, including
composer and emotion classification, music generation, and sequence
representation learning. We demonstrate that explicit information leads to
better results depending on the task.