  This study focuses on generating fundamental frequency (F0) curves of singing
voice from musical scores stored in a midi-like notation. Current statistical
parametric approaches to singing F0 modeling meet difficulties in reproducing
vibratos and the temporal details at note boundaries due to the oversmoothing
tendency of statistical models. This paper presents a neural network based
solution that models a pair of neighboring notes at a time (the transition
model) and uses a separate network for generating vibratos (the sustain model).
Predictions from the two models are combined by summation after proper
enveloping to enforce continuity. In the training phase, mild misalignment
between the scores and the target F0 is addressed by back-propagating the
gradients to the networks' inputs. Subjective listening tests on the NITech
singing database show that transition-sustain models are able to generate F0
trajectories close to the original performance.
