  We introduce a data-driven approach to automatic pitch correction of solo
singing performances. The proposed approach predicts note-wise pitch shifts
from the relationship between the respective spectrograms of the singing and
accompaniment. This approach differs from commercial systems, where vocal track
notes are usually shifted to be centered around pitches in a user-defined
score, or mapped to the closest pitch among the twelve equal-tempered scale
degrees. The proposed system treats pitch as a continuous value rather than
relying on a set of discretized notes found in musical scores, thus allowing
for improvisation and harmonization in the singing performance. We train our
neural network model using a dataset of 4,702 amateur karaoke performances
selected for good intonation. Our model is trained on both incorrect
intonation, for which it learns a correction, and intentional pitch variation,
which it learns to preserve. The proposed deep neural network with gated
recurrent units on top of convolutional layers shows promising performance on
the real-world score-free singing pitch correction task of autotuning.
