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We introduce a data-driven approach to automatic pitch correction of solosinging performances. The proposed approach predicts note-wise pitch shiftsfrom the relationship between the respective spectrograms of the singing andaccompaniment. This approach differs from commercial systems, where vocal tracknotes are usually shifted to be centered around pitches in a user-definedscore, or mapped to the closest pitch among the twelve equal-tempered scaledegrees. The proposed system treats pitch as a continuous value rather thanrelying on a set of discretized notes found in musical scores, thus allowingfor improvisation and harmonization in the singing performance. We train ourneural network model using a dataset of 4,702 amateur karaoke performancesselected for good intonation. Our model is trained on both incorrectintonation, for which it learns a correction, and intentional pitch variation,which it learns to preserve. The proposed deep neural network with gatedrecurrent units on top of convolutional layers shows promising performance onthe real-world score-free singing pitch correction task of autotuning.

Deep Autotuner: a Pitch Correcting Network for Singing Performances