Pruning masks from dynamic channel pruning in speech enhancement networks encode enough information for simple predictors to achieve 93% VAD accuracy, 84% noise classification accuracy, and R2=0.86 on F0 estimation with negligible overhead.
Deep Learning-Based Non-Intrusive Multi-Objective Speech Assessment Model With Cross-Domain Features,
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From Diet to Free Lunch: Estimating Auxiliary Signal Properties using Dynamic Pruning Masks in Speech Enhancement Networks
Pruning masks from dynamic channel pruning in speech enhancement networks encode enough information for simple predictors to achieve 93% VAD accuracy, 84% noise classification accuracy, and R2=0.86 on F0 estimation with negligible overhead.