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.
TF-GridNet: Making time-frequency domain models great again for monaural speaker separation,
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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.