  This paper introduces the Multi-Band Excited WaveNet a neural vocoder for
speaking and singing voices. It aims to advance the state of the art towards an
universal neural vocoder, which is a model that can generate voice signals from
arbitrary mel spectrograms extracted from voice signals. Following the success
of the DDSP model and following the development of the recently proposed
excitation vocoders we propose a vocoder structure consisting of multiple
specialized DNN that are combined with dedicated signal processing components.
All components are implemented as differentiable operators and therefore allow
joined optimization of the model parameters. To prove the capacity of the model
to reproduce high quality voice signals we evaluate the model on single and
multi speaker/singer datasets. We conduct a subjective evaluation demonstrating
that the models support a wide range of domain variations (unseen voices,
languages, expressivity) achieving perceptive quality that compares with a
state of the art universal neural vocoder, however using significantly smaller
training datasets and significantly less parameters. We also demonstrate
remaining limits of the universality of neural vocoders e.g. the creation of
saturated singing voices.
