  We present UTACO, a singing synthesis model based on an attention-based
sequence-to-sequence mechanism and a vocoder based on dilated causal
convolutions. These two classes of models have significantly affected the field
of text-to-speech, but have never been thoroughly applied to the task of
singing synthesis. UTACO demonstrates that attention can be successfully
applied to the singing synthesis field and improves naturalness over the state
of the art. The system requires considerably less explicit modelling of voice
features such as F0 patterns, vibratos, and note and phoneme durations, than
previous models in the literature. Despite this, it shows a strong improvement
in naturalness with respect to previous neural singing synthesis models. The
model does not require any durations or pitch patterns as inputs, and learns to
insert vibrato autonomously according to the musical context. However, we
observe that, by completely dispensing with any explicit duration modelling it
becomes harder to obtain the fine control of timing needed to exactly match the
tempo of a song.
