Single-channel methods for the separation of the lead vocal from mixed audio have traditionally included harmonicsinusoidal modeling and matrix decomposition methods, each with its own strengths and shortcomings. In this work we use a hybrid framework to incorporate prior knowledge about singer and phone identity to achieve the superior separation of the lead vocal from the instrumental background. Singer specific dictionaries learned from available polyphonic recordings provide the soft mask that effectively attenuates the bleeding-through of accompanying melodic instruments typical of purely harmonic-sinusoidal model based separation. The dictionary learning uses NMF optimization across a training set of mixed signal utterances while keeping the vocal signal bases constant across the utterances. A soft mask is determined for each test mixed utterance frame by imposing sparseness constraints in the NMF partial co-factorization. We demonstrate significant improvements in reconstructed signal quality arising from the more accurate estimation of singer-vowel spectral envelope.
['Shrikant Venkataramani', 'Nagesh Nayak', 'Preeti Rao', 'Rajbabu Velmurugan']
VOCAL SEPARATION USING SINGER-VOWEL PRIORS OBTAINED FROM POLYPHONIC AUDIO