Expressive Acoustic Guitar Sound Synthesis with an Instrument-Specific
  Input Representation and Diffusion Outpainting
Hounsu Kim Soonbeom Choi Juhan Nam
Synthesizing performing guitar sound is a highly challenging task due to the
polyphony and high variability in expression. Recently, deep generative models
have shown promising results in synthesizing expressive polyphonic instrument
sounds from music scores, often using a generic MIDI input. In this work, we
propose an expressive acoustic guitar sound synthesis model with a customized
input representation to the instrument, which we call guitarroll. We implement
the proposed approach using diffusion-based outpainting which can generate
audio with long-term consistency. To overcome the lack of MIDI/audio-paired
datasets, we used not only an existing guitar dataset but also collected data
from a high quality sample-based guitar synthesizer. Through quantitative and
qualitative evaluations, we show that our proposed model has higher audio
quality than the baseline model and generates more realistic timbre sounds than
the previous leading work.