  We present a new model for singing synthesis based on a modified version of
the WaveNet architecture. Instead of modeling raw waveform, we model features
produced by a parametric vocoder that separates the influence of pitch and
timbre. This allows conveniently modifying pitch to match any target melody,
facilitates training on more modest dataset sizes, and significantly reduces
training and generation times. Our model makes frame-wise predictions using
mixture density outputs rather than categorical outputs in order to reduce the
required parameter count. As we found overfitting to be an issue with the
relatively small datasets used in our experiments, we propose a method to
regularize the model and make the autoregressive generation process more robust
to prediction errors. Using a simple multi-stream architecture, harmonic,
aperiodic and voiced/unvoiced components can all be predicted in a coherent
manner. We compare our method to existing parametric statistical and
state-of-the-art concatenative methods using quantitative metrics and a
listening test. While naive implementations of the autoregressive generation
algorithm tend to be inefficient, using a smart algorithm we can greatly speed
up the process and obtain a system that's competitive in both speed and
quality.
