SingAug: Data Augmentation for Singing Voice Synthesis with Cycle-consistent Training Strategy

Shuai Guo, Jiatong Shi, Tao Qian, Shinji Watanabe, Qin Jin*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Deep learning based singing voice synthesis (SVS) systems have been demonstrated to flexibly generate singing with better qualities, compared to conventional statistical parametric based methods. However, neural systems are generally data-hungry and have difficulty to reach reasonable singing quality with limited public available training data. In this work, we explore different data augmentation methods to boost the training of SVS systems, including several strategies customized to SVS based on pitch augmentation and mix-up augmentation. To further stabilize the training, we introduce the cycle-consistent training strategy. Extensive experiments on two public singing databases demonstrate that our proposed augmentation methods and the stabilizing training strategy can significantly improve the performance on both objective and subjective evaluations.

Original languageEnglish
Pages (from-to)4272-4276
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2022-September
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022 - Incheon, Korea, Republic of
Duration: 2022 Sep 182022 Sep 22

Keywords

  • cycle-consistent training strategy
  • data augmentation
  • singing voice synthesis

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

Fingerprint

Dive into the research topics of 'SingAug: Data Augmentation for Singing Voice Synthesis with Cycle-consistent Training Strategy'. Together they form a unique fingerprint.

Cite this