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Found 2 papers, 1 papers with code
Unsupervised Music Source Separation Using Differentiable Parametric Source Models
Integrating domain knowledge in the form of source models into a data-driven method leads to high data efficiency: the proposed approach achieves good separation quality even when trained on less than three minutes of audio.
Interleaved Multitask Learning for Audio Source Separation with Independent Databases
We propose an interleaved training procedure that optimizes the sub-task decoders independently and thus does not require each sample to possess a ground truth for all of its composing sources.
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