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

2022-11-25 02:54:33
Xuan Shi, Erica Cooper, Xin Wang, Junichi Yamagishi, Shrikanth Narayanan
arXiv_SD
     
Abstract

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 and pre-trained models are open sourced at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2211.13868

PDF

https://arxiv.org/pdf/2211.13868.pdf

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