论文标题
Singaug：通过周期符合培训策略的唱歌语音综合的数据增强
SingAug: Data Augmentation for Singing Voice Synthesis with Cycle-consistent Training Strategy
论文作者
论文摘要
与传统的基于统计参数的方法相比，已经证明了基于深度学习的歌声综合（SVS）系统具有更高的质量唱歌。但是，神经系统通常是渴望数据的，并且很难通过有限的公共可用培训数据来达到合理的歌唱质量。在这项工作中，我们探索了不同的数据增强方法，以增强SVS系统的培训，包括基于俯仰扩大和混合增强为SVS定制的几种策略。为了进一步稳定培训，我们介绍了循环一致的培训策略。在两个公开唱歌数据库上进行的广泛实验表明，我们提出的增强方法和稳定训练策略可以显着提高客观和主观评估的绩效。
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.
