  With the similarity between music and speech synthesis from symbolic input
and the rapid development of text-to-speech (TTS) techniques, it is worthwhile
to explore ways to improve the MIDI-to-audio performance by borrowing from TTS
techniques. In this study, we analyze the shortcomings of a TTS-based
MIDI-to-audio system and improve it in terms of feature computation, model
selection, and training strategy, aiming to synthesize highly natural-sounding
audio. Moreover, we conducted an extensive model evaluation through listening
tests, pitch measurement, and spectrogram analysis. This work demonstrates not
only synthesis of highly natural music but offers a thorough analytical
approach and useful outcomes for the community. Our code, pre-trained models,
supplementary materials, and audio samples are open sourced at
https://github.com/nii-yamagishilab/midi-to-audio.
