  Learning musical structures and composition patterns is necessary for both
music generation and understanding, but current methods do not make uniform use
of learned features to generate and comprehend music simultaneously. In this
paper, we propose PianoBART, a pre-trained model that uses BART for both
symbolic piano music generation and understanding. We devise a multi-level
object selection strategy for different pre-training tasks of PianoBART, which
can prevent information leakage or loss and enhance learning ability. The
musical semantics captured in pre-training are fine-tuned for music generation
and understanding tasks. Experiments demonstrate that PianoBART efficiently
learns musical patterns and achieves outstanding performance in generating
high-quality coherent pieces and comprehending music. Our code and
supplementary material are available at https://github.com/RS2002/PianoBart.
