Sequence-to-sequence Singing Voice Synthesis with Perceptual Entropy Loss

Jiatong Shi; Shuai Guo; Nan Huo; Yuekai Zhang; Qin Jin

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arXiv · 2010.12024

Sequence-to-sequence Singing Voice Synthesis with Perceptual Entropy Loss

Jiatong Shi·Shuai Guo·Nan Huo·Yuekai Zhang·Qin Jin

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Abstract

The neural network (NN) based singing voice synthesis (SVS) systems require sufficient data to train well and are prone to over-fitting due to data scarcity. However, we often encounter data limitation problem in building SVS systems because of high data acquisition and annotation costs. In this work, we propose a Perceptual Entropy (PE) loss derived from a psycho-acoustic hearing model to regularize the network. With a one-hour open-source singing voice database, we explore the impact of the PE loss on various mainstream sequence-to-sequence models, including the RNN-based, transformer-based, and conformer-based models. Our experiments show that the PE loss can mitigate the over-fitting problem and significantly improve the synthesized singing quality reflected in objective and subjective evaluations.

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BibTeX RIS

Jiatong Shi, Shuai Guo, Nan Huo, Yuekai Zhang, Qin Jin. 2020-10-22. Sequence-to-sequence Singing Voice Synthesis with Perceptual Entropy Loss. https://arxiv.org/abs/2010.12024

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