  Most recent research about automatic music transcription (AMT) uses
convolutional neural networks and recurrent neural networks to model the
mapping from music signals to symbolic notation. Based on a high-resolution
piano transcription system, we explore the possibility of incorporating another
powerful sequence transformation tool -- the Transformer -- to deal with the
AMT problem. We argue that the properties of the Transformer make it more
suitable for certain AMT subtasks. We confirm the Transformer's superiority on
the velocity detection task by experiments on the MAESTRO dataset and a
cross-dataset evaluation on the MAPS dataset. We observe a performance
improvement on both frame-level and note-level metrics after introducing the
Transformer network.
