  We are interested in the task of generating multi-instrumental music scores.
The Transformer architecture has recently shown great promise for the task of
piano score generation; here we adapt it to the multi-instrumental setting.
Transformers are complex, high-dimensional language models which are capable of
capturing long-term structure in sequence data, but require large amounts of
data to fit. Their success on piano score generation is partially explained by
the large volumes of symbolic data readily available for that domain. We
leverage the recently-introduced NES-MDB dataset of four-instrument scores from
an early video game sound synthesis chip (the NES), which we find to be
well-suited to training with the Transformer architecture. To further improve
the performance of our model, we propose a pre-training technique to leverage
the information in a large collection of heterogeneous music, namely the Lakh
MIDI dataset. Despite differences between the two corpora, we find that this
transfer learning procedure improves both quantitative and qualitative
performance for our primary task.
