  We conduct an investigation on various hyper-parameters regarding neural
networks used to generate spectral envelopes for singing synthesis. Two
perceptive tests, where the first compares two models directly and the other
ranks models with a mean opinion score, are performed. With these tests we show
that when learning to predict spectral envelopes, 2d-convolutions are superior
over previously proposed 1d-convolutions and that predicting multiple frames in
an iterated fashion during training is superior over injecting noise to the
input data. An experimental investigation whether learning to predict a
probability distribution vs.\ single samples was performed but turned out to be
inconclusive. A network architecture is proposed that incorporates the
improvements which we found to be useful and we show in our experiments that
this network produces better results than other stat-of-the-art methods.
