no code implementations • 5 Jun 2023 • Marvin Sach, Jan Franzen, Bruno Defraene, Kristoff Fluyt, Maximilian Strake, Wouter Tirry, Tim Fingscheidt
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).
no code implementations • 4 May 2022 • Ziyi Xu, Maximilian Strake, Tim Fingscheidt
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.
no code implementations • 6 Nov 2021 • Ziyi Xu, Maximilian Strake, Tim Fingscheidt
Perceptual evaluation of speech quality (PESQ) is a widely used metric for evaluating speech quality.
no code implementations • 31 Mar 2021 • Ernst Seidel, Jan Franzen, Maximilian Strake, Tim Fingscheidt
The proposed models achieved remarkable performance for the separate tasks of AEC and residual echo suppression (RES).
no code implementations • 31 Mar 2021 • Ziyi Xu, Maximilian Strake, Tim Fingscheidt
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.