  We present in this paper PerformacnceNet, a neural network model we proposed
recently to achieve score-to-audio music generation. The model learns to
convert a music piece from the symbolic domain to the audio domain, assigning
performance-level attributes such as changes in velocity automatically to the
music and then synthesizing the audio. The model is therefore not just a neural
audio synthesizer, but an AI performer that learns to interpret a musical score
in its own way. The code and sample outputs of the model can be found online at
https://github.com/bwang514/PerformanceNet.
