  Singing voice synthesis (SVS) is the computer production of a human-like
singing voice from given musical scores. To accomplish end-to-end SVS
effectively and efficiently, this work adopts the acoustic model-neural vocoder
architecture established for high-quality speech and singing voice synthesis.
Specifically, this work aims to pursue a higher level of expressiveness in
synthesized voices by combining the diffusion denoising probabilistic model
(DDPM) and \emph{Wasserstein} generative adversarial network (WGAN) to
construct the backbone of the acoustic model. On top of the proposed acoustic
model, a HiFi-GAN neural vocoder is adopted with integrated fine-tuning to
ensure optimal synthesis quality for the resulting end-to-end SVS system. This
end-to-end system was evaluated with the multi-singer Mpop600 Mandarin singing
voice dataset. In the experiments, the proposed system exhibits improvements
over previous landmark counterparts in terms of musical expressiveness and
high-frequency acoustic details. Moreover, the adversarial acoustic model
converged stably without the need to enforce reconstruction objectives,
indicating the convergence stability of the proposed DDPM and WGAN combined
architecture over alternative GAN-based SVS systems.
