  This paper proposes a new self-attention based model for music score
infilling, i.e., to generate a polyphonic music sequence that fills in the gap
between given past and future contexts. While existing approaches can only fill
in a short segment with a fixed number of notes, or a fixed time span between
the past and future contexts, our model can infill a variable number of notes
(up to 128) for different time spans. We achieve so with three major technical
contributions. First, we adapt XLNet, an autoregressive model originally
proposed for unsupervised model pre-training, to music score infilling. Second,
we propose a new, musically specialized positional encoding called relative bar
encoding that better informs the model of notes' position within the past and
future context. Third, to capitalize relative bar encoding, we perform
look-ahead onset prediction to predict the onset of a note one time step before
predicting the other attributes of the note. We compare our proposed model with
two strong baselines and show that our model is superior in both objective and
subjective analyses.
