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.
Torchaudio-Squim: Reference-Less Speech Quality and Intelligibility Measures in Torchaudio,
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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.