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We study the problem of semi-supervised singing voice separation, in whichthe training data contains a set of samples of mixed music (singing andinstrumental) and an unmatched set of instrumental music. Our solution employsa single mapping function g, which, applied to a mixed sample, recovers theunderlying instrumental music, and, applied to an instrumental sample, returnsthe same sample. The network g is trained using purely instrumental samples, aswell as on synthetic mixed samples that are created by mixing reconstructedsinging voices with random instrumental samples. Our results indicate that weare on a par with or better than fully supervised methods, which are alsoprovided with training samples of unmixed singing voices, and are better thanother recent semi-supervised methods.

Semi-Supervised Monaural Singing Voice Separation With a Masking Network Trained on Synthetic Mixtures