  This paper presents a end-to-end framework for the F0 transformation in the
context of expressive voice conversion. A single neural network is proposed, in
which a first module is used to learn F0 representation over different temporal
scales and a second adversarial module is used to learn the transformation from
one emotion to another. The first module is composed of a convolution layer
with wavelet kernels so that the various temporal scales of F0 variations can
be efficiently encoded. The single decomposition/transformation network allows
to learn in a end-to-end manner the F0 decomposition that are optimal with
respect to the transformation, directly from the raw F0 signal.
