  With the rapid development of neural network architectures and speech
processing models, singing voice synthesis with neural networks is becoming the
cutting-edge technique of digital music production. In this work, in order to
explore how to improve the quality and efficiency of singing voice synthesis,
in this work, we use encoder-decoder neural models and a number of vocoders to
achieve singing voice synthesis. We conduct experiments to demonstrate that the
models can be trained using voice data with pitch information, lyrics and beat
information, and the trained models can produce smooth, clear and natural
singing voice that is close to real human voice. As the models work in the
end-to-end manner, they allow users who are not domain experts to directly
produce singing voice by arranging pitches, lyrics and beats.
