  We explore the use of neural synthesis for acoustic guitar from string-wise
MIDI input. We propose four different systems and compare them with both
objective metrics and subjective evaluation against natural audio and a
sample-based baseline. We iteratively develop these four systems by making
various considerations on the architecture and intermediate tasks, such as
predicting pitch and loudness control features. We find that formulating the
control feature prediction task as a classification task rather than a
regression task yields better results. Furthermore, we find that our simplest
proposed system, which directly predicts synthesis parameters from MIDI input
performs the best out of the four proposed systems. Audio examples are
available at https://erl-j.github.io/neural-guitar-web-supplement.
