Soft constrained leading voice separation with music score guidance

Separating leading voice from a music mixture remains challenging for automatic systems. Competing harmonics from music accompaniment severely interfere the leading voice estimation. To properly extract the leading voice, separation algorithms based on source-filter modeling of human voice and non-negative matrix factorization have been introduced. This paper extends this approach with a statistical weighting scheme to rank various pitch candidates with music score information. It imposes a soft constraint on the likelihood of these pitch candidates, so the interference from music accompaniment on leading voice estimation is reduced. Our experiments showed that this soft-constrained separation with score guidance provides accurate inference about the leading vocal pitch with reliable score and remains robust for erroneous score.