  In this paper, we explore the tokenized representation of musical scores
using the Transformer model to automatically generate musical scores. Thus far,
sequence models have yielded fruitful results with note-level (MIDI-equivalent)
symbolic representations of music. Although the note-level representations can
comprise sufficient information to reproduce music aurally, they cannot contain
adequate information to represent music visually in terms of notation. Musical
scores contain various musical symbols (e.g., clef, key signature, and notes)
and attributes (e.g., stem direction, beam, and tie) that enable us to visually
comprehend musical content. However, automated estimation of these elements has
yet to be comprehensively addressed. In this paper, we first design score token
representation corresponding to the various musical elements. We then train the
Transformer model to transcribe note-level representation into appropriate
music notation. Evaluations of popular piano scores show that the proposed
method significantly outperforms existing methods on all 12 musical aspects
that were investigated. We also explore an effective notation-level token
representation to work with the model and determine that our proposed
representation produces the steadiest results.
