  In this paper, we present a machine-learning approach to pitch correction for
voice in a karaoke setting, where the vocals and accompaniment are on separate
tracks and time-aligned. The network takes as input the time-frequency
representation of the two tracks and predicts the amount of pitch-shifting in
cents required to make the voice sound in-tune with the accompaniment. It is
trained on examples of semi-professional singing. The proposed approach differs
from existing real-time pitch correction methods by replacing pitch tracking
and mapping to a discrete set of notes---for example, the twelve classes of the
equal-tempered scale---with learning a correction that is continuous both in
frequency and in time directly from the harmonics of the vocal and
accompaniment tracks. A Recurrent Neural Network (RNN) model provides a
correction that takes context into account, preserving expressive pitch bending
and vibrato. This method can be extended into unsupervised pitch correction of
a vocal performance---popularly referred to as autotuning.
