  This paper explores sequential modelling of polyphonic music with deep neural
networks. While recent breakthroughs have focussed on network architecture, we
demonstrate that the representation of the sequence can make an equally
significant contribution to the performance of the model as measured by
validation set loss. By extracting salient features inherent to the training
dataset, the model can either be conditioned on these features or trained to
predict said features as extra components of the sequences being modelled. We
show that training a neural network to predict a seemingly more complex
sequence, with extra features included in the series being modelled, can
improve overall model performance significantly. We first introduce TonicNet, a
GRU-based model trained to initially predict the chord at a given time-step
before then predicting the notes of each voice at that time-step, in contrast
with the typical approach of predicting only the notes. We then evaluate
TonicNet on the canonical JSB Chorales dataset and obtain state-of-the-art
results.
