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We describe a machine-learning approach to pitch correcting a solo singingperformance in a karaoke setting, where the solo voice and accompaniment are onseparate tracks. The proposed approach addresses the situation where no musicalscore of the vocals nor the accompaniment exists: It predicts the amount ofcorrection from the relationship between the spectral contents of the vocal andaccompaniment tracks. Hence, the pitch shift in cents suggested by the modelcan be used to make the voice sound in tune with the accompaniment. Thisapproach differs from commercially used automatic pitch correction systems,where notes in the vocal tracks are shifted to be centered around notes in auser-defined score or mapped to the closest pitch among the twelveequal-tempered scale degrees. We train the model using a dataset of 4,702amateur karaoke performances selected for good intonation. We present aConvolutional Gated Recurrent Unit (CGRU) model to accomplish this task. Thismethod can be extended into unsupervised pitch correction of a vocalperformance, popularly referred to as autotuning.

Deep Autotuner: A Data-Driven Approach to Natural-Sounding Pitch Correction for Singing Voice in Karaoke Performances