  We present a deep neural network based singing voice synthesizer, inspired by
the Deep Convolutions Generative Adversarial Networks (DCGAN) architecture and
optimized using the Wasserstein-GAN algorithm. We use vocoder parameters for
acoustic modelling, to separate the influence of pitch and timbre. This
facilitates the modelling of the large variability of pitch in the singing
voice. Our network takes a block of consecutive frame-wise linguistic and
fundamental frequency features, along with global singer identity as input and
outputs vocoder features, corresponding to the block of features. This
block-wise approach, along with the training methodology allows us to model
temporal dependencies within the features of the input block. For inference,
sequential blocks are concatenated using an overlap-add procedure. We show that
the performance of our model is competitive with regards to the
state-of-the-art and the original sample using objective metrics and a
subjective listening test. We also present examples of the synthesis on a
supplementary website and the source code via GitHub.
