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Found 5 papers, 0 papers with code
EffCRN: An Efficient Convolutional Recurrent Network for High-Performance Speech Enhancement
By applying a number of topological changes at once, we propose both an efficient FCRN (FCRN15), and a new family of efficient convolutional recurrent neural networks (EffCRN23, EffCRN23lite).
Does a PESQNet (Loss) Require a Clean Reference Input? The Original PESQ Does, But ACR Listening Tests Don't
Detailed analyses show that the DNS trained with the MF-intrusive PESQNet outperforms the Interspeech 2021 DNS Challenge baseline and the same DNS trained with an MSE loss by 0. 23 and 0. 12 PESQ points, respectively.
Deep Noise Suppression Maximizing Non-Differentiable PESQ Mediated by a Non-Intrusive PESQNet
Perceptual evaluation of speech quality (PESQ) is a widely used metric for evaluating speech quality.
Y$^2$-Net FCRN for Acoustic Echo and Noise Suppression
The proposed models achieved remarkable performance for the separate tasks of AEC and residual echo suppression (RES).
Deep Noise Suppression With Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data
During the training process, most of the speech enhancement neural networks are trained in a fully supervised way with losses requiring noisy speech to be synthesized by clean speech and additive noise.
