Search Results for author:
Found 4 papers, 0 papers with code
Investigating the Design Space of Diffusion Models for Speech Enhancement
To address this, we extend this framework to account for the progressive transformation between the clean and noisy speech signals.
Diffusion-Based Speech Enhancement in Matched and Mismatched Conditions Using a Heun-Based Sampler
We show that the proposed system substantially benefits from using multiple databases for training, and achieves superior performance compared to state-of-the-art discriminative models in both matched and mismatched conditions.
Assessing the Generalization Gap of Learning-Based Speech Enhancement Systems in Noisy and Reverberant Environments
We find that for all models, the performance degrades the most in speech mismatches, while good noise and room generalization can be achieved by training on multiple databases.
On Batching Variable Size Inputs for Training End-to-End Speech Enhancement Systems
This paper systematically investigates the effect of different batching strategies and batch sizes on the training statistics and speech enhancement performance of a Conv-TasNet, evaluated in both matched and mismatched conditions.
