  The problem of audio synthesis has been increasingly solved using deep neural
networks. With the introduction of Generative Adversarial Networks (GAN),
another efficient and adjective path has opened up to solve this problem. In
this paper, we present a method to synthesize the singing voice of a person
using a Convolutional Long Short-term Memory (ConvLSTM) based GAN optimized
using the Wasserstein loss function. Our work is inspired by WGANSing by
Chandna et al. Our model inputs consecutive frame-wise linguistic and frequency
features, along with singer identity and outputs vocoder features. We train the
model on a dataset of 48 English songs sung and spoken by 12 non-professional
singers. For inference, sequential blocks are concatenated using an overlap-add
procedure. We test the model using the Mel-Cepstral Distance metric and a
subjective listening test with 18 participants.
