  Pitch detection is a fundamental problem in speech processing as F0 is used
in a large number of applications. Recent articles have proposed deep learning
for robust pitch tracking. In this paper, we consider voicing detection as a
classification problem and F0 contour estimation as a regression problem. For
both tasks, acoustic features from multiple domains and traditional machine
learning methods are used. The discrimination power of existing and proposed
features is assessed through mutual information. Multiple supervised and
unsupervised approaches are compared. A significant relative reduction of
voicing errors over the best baseline is obtained: 20% with the best clustering
method (K-means) and 45% with a Multi-Layer Perceptron. For F0 contour
estimation, the benefits of regression techniques are limited though. We
investigate whether those objective gains translate in a parametric synthesis
task. Clear perceptual preferences are observed for the proposed approach over
two widely-used baselines (RAPT and DIO).
