Singing voice conversion aims to convert singer's voice from source to target
without changing singing content. Parallel training data is typically required
for the training of singing voice conversion system, that is however not
practical in real-life applications. Recent encoder-decoder structures, such as
variational autoencoding Wasserstein generative adversarial network (VAW-GAN),
provide an effective way to learn a mapping through non-parallel training data.
In this paper, we propose a singing voice conversion framework that is based on
VAW-GAN. We train an encoder to disentangle singer identity and singing prosody
(F0 contour) from phonetic content. By conditioning on singer identity and F0,
the decoder generates output spectral features with unseen target singer
identity, and improves the F0 rendering. Experimental results show that the
proposed framework achieves better performance than the baseline frameworks.

本论文提出了一种基于 VAW-GAN 的歌声转换框架，通过训练一个编码器来解开歌手身份和演唱语调（F0 曲线），通过对歌手身份和 F0 进行条件生成，解码器能够生成具有未见目标歌手身份的输出谱特征，并提高了 F0 渲染的性能。

使用非并行训练数据的 VAW-GAN 歌声转化

VAW-GAN for Singing Voice Conversion with Non-parallel Training Data

This paper presents a method of sequence-to-sequence (seq2seq) voice
conversion using non-parallel training data. In this method, disentangled
linguistic and speaker representations are extracted from acoustic features,
and voice conversion is achieved by preserving the linguistic representations
of source utterances while replacing the speaker representations with the
target ones. Our model is built under the framework of encoder-decoder neural
networks. A recognition encoder is designed to learn the disentangled
linguistic representations with two strategies. First, phoneme transcriptions
of training data are introduced to provide the references for leaning
linguistic representations of audio signals. Second, an adversarial training
strategy is employed to further wipe out speaker information from the
linguistic representations. Meanwhile, speaker representations are extracted
from audio signals by a speaker encoder. The model parameters are estimated by
two-stage training, including a pretraining stage using a multi-speaker dataset
and a fine-tuning stage using the dataset of a specific conversion pair. Since
both the recognition encoder and the decoder for recovering acoustic features
are seq2seq neural networks, there are no constrains of frame alignment and
frame-by-frame conversion in our proposed method. Experimental results showed
that our method obtained higher similarity and naturalness than the best
non-parallel voice conversion method in Voice Conversion Challenge 2018.
Besides, the performance of our proposed method was closed to the
state-of-the-art parallel seq2seq voice conversion method.

本文提出了一种使用非并行训练数据的序列到序列（seq2seq）语音转换方法，该方法使用编码器 - 解码器神经网络框架构建模型，从声学特征中提取解交叉的语言和说话者表示，并通过保留源话语的语言表示，将说话者表示替换为目标音频的表示来实现语音转换。实验结果表明，该方法在语音转换挑战 2018 中得到了比最佳非并行语音转换方法更高的相似度和自然程度。