A Fully Differentiable Model for Unsupervised Singing Voice Separation
Gaël Richard,
Pierre Chouteau,
Bernardo Torres
Abstract:A novel model was recently proposed by for unsupervised music source separation. This model allows to tackle some of the major shortcomings of existing source separation frameworks. Specifically, it eliminates the need for isolated sources during training, performs efficiently with limited data, and can handle homogeneous sources (such as singing voice). But, this model relies on an external multipitch estimator and incorporates an Ad hoc voice assignment procedure. In this paper, we propose to extend this fr… Show more
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