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With the similarity between music and speech synthesis from symbolic inputand the rapid development of text-to-speech (TTS) techniques, it is worthwhileto explore ways to improve the MIDI-to-audio performance by borrowing from TTStechniques. In this study, we analyze the shortcomings of a TTS-basedMIDI-to-audio system and improve it in terms of feature computation, modelselection, and training strategy, aiming to synthesize highly natural-soundingaudio. Moreover, we conducted an extensive model evaluation through listeningtests, pitch measurement, and spectrogram analysis. This work demonstrates notonly synthesis of highly natural music but offers a thorough analyticalapproach and useful outcomes for the community. Our code, pre-trained models,supplementary materials, and audio samples are open sourced athttps://github.com/nii-yamagishilab/midi-to-audio.

Can Knowledge of End-to-End Text-to-Speech Models Improve Neural MIDI-to-Audio Synthesis Systems?