Ring mixing and SCER loss break symmetry in noisy speech separation training, allowing models to learn denoising from noisy mixtures alone and halve residual noise on benchmarks.
A dilemma of ground truth in noisy speech separation and an ap- proach to lessen the impact of imperfect training data,
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Ring Mixing with Auxiliary Signal-to-Consistency-Error Ratio Loss for Unsupervised Denoising in Speech Separation
Ring mixing and SCER loss break symmetry in noisy speech separation training, allowing models to learn denoising from noisy mixtures alone and halve residual noise on benchmarks.