The symbolic music modality is nowadays mostly represented as discrete and
used with sequential models such as Transformers, for deep learning tasks.
Recent research put efforts on the tokenization, i.e. the conversion of data
into sequences of integers intelligible to such models. This can be achieved by
many ways as music can be composed of simultaneous tracks, of simultaneous
notes with several attributes. Until now, the proposed tokenizations are based
on small vocabularies describing the note attributes and time events, resulting
in fairly long token sequences. In this paper, we show how Byte Pair Encoding
(BPE) can improve the results of deep learning models while improving its
performances. We experiment on music generation and composer classification,
and study the impact of BPE on how models learn the embeddings, and show that
it can help to increase their isotropy, i.e., the uniformity of the variance of
their positions in the space.

本文研究在深度学习任务中，如何使用符号音乐模态并采用 Byte Pair Encoding (BPE) 对音乐数据进行字符编码，以提高音乐生成和作曲家分类模型的性能和均匀度。