Singing voice conversion is converting the timbre in the source singing to
the target speaker's voice while keeping singing content the same. However,
singing data for target speaker is much more difficult to collect compared with
normal speech data.In this paper, we introduce a singing voice conversion
algorithm that is capable of generating high quality target speaker's singing
using only his/her normal speech data. First, we manage to integrate the
training and conversion process of speech and singing into one framework by
unifying the features used in standard speech synthesis system and singing
synthesis system. In this way, normal speech data can also contribute to
singing voice conversion training, making the singing voice conversion system
more robust especially when the singing database is small.Moreover, in order to
achieve one-shot singing voice conversion, a speaker embedding module is
developed using both speech and singing data, which provides target speaker
identify information during conversion. Experiments indicate proposed sing
conversion system can convert source singing to target speaker's high-quality
singing with only 20 seconds of target speaker's enrollment speech data.

本文介绍了一种歌声转换算法，可以使用目标讲话者的正常语音数据生成高质量的目标讲话者歌声，并大大提高了歌声转换系统的鲁棒性和转换效率。

DurIAN-SC: 能够考虑音长信息的歌声转换注意力网络系统

DurIAN-SC: Duration Informed Attention Network based Singing Voice  Conversion System

We present UTACO, a singing synthesis model based on an attention-based
sequence-to-sequence mechanism and a vocoder based on dilated causal
convolutions. These two classes of models have significantly affected the field
of text-to-speech, but have never been thoroughly applied to the task of
singing synthesis. UTACO demonstrates that attention can be successfully
applied to the singing synthesis field and improves naturalness over the state
of the art. The system requires considerably less explicit modelling of voice
features such as F0 patterns, vibratos, and note and phoneme durations, than
previous models in the literature. Despite this, it shows a strong improvement
in naturalness with respect to previous neural singing synthesis models. The
model does not require any durations or pitch patterns as inputs, and learns to
insert vibrato autonomously according to the musical context. However, we
observe that, by completely dispensing with any explicit duration modelling it
becomes harder to obtain the fine control of timing needed to exactly match the
tempo of a song.

UTACO 是一种基于注意力机制和精简的神经网络构建的歌唱合成模型，相比前人的模型需要的人声特征建模较少，自主学习添加音乐背景下的 vibrato，但完全放弃了时间建模会影响音乐节奏的精度。

歌声合成：借助注意力的一点帮助

Singing Synthesis: with a little help from my attention

We present a new model for singing synthesis based on a modified version of
the WaveNet architecture. Instead of modeling raw waveform, we model features
produced by a parametric vocoder that separates the influence of pitch and
timbre. This allows conveniently modifying pitch to match any target melody,
facilitates training on more modest dataset sizes, and significantly reduces
training and generation times. Our model makes frame-wise predictions using
mixture density outputs rather than categorical outputs in order to reduce the
required parameter count. As we found overfitting to be an issue with the
relatively small datasets used in our experiments, we propose a method to
regularize the model and make the autoregressive generation process more robust
to prediction errors. Using a simple multi-stream architecture, harmonic,
aperiodic and voiced/unvoiced components can all be predicted in a coherent
manner. We compare our method to existing parametric statistical and
state-of-the-art concatenative methods using quantitative metrics and a
listening test. While naive implementations of the autoregressive generation
algorithm tend to be inefficient, using a smart algorithm we can greatly speed
up the process and obtain a system that's competitive in both speed and
quality.

利用基于 WaveNet 架构的新型合成模型，对参数声码器产生的特征进行建模，并使用混合密度输出，实现对每个帧的预测，避免了过度拟合，在预测错误的情况下进行自回归生成算法的正则化，成功将和声、非周期性和有声 / 无声组件预测在一起，比现有的参数化统计方法和拼接方法更为有效。