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Found 4 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).
Deep Residual Echo Suppression and Noise Reduction: A Multi-Input FCRN Approach in a Hybrid Speech Enhancement System
Deep neural network (DNN)-based approaches to acoustic echo cancellation (AEC) and hybrid speech enhancement systems have gained increasing attention recently, introducing significant performance improvements to this research field.
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).
AEC in a NetShell: On Target and Topology Choices for FCRN Acoustic Echo Cancellation
Acoustic echo cancellation (AEC) algorithms have a long-term steady role in signal processing, with approaches improving the performance of applications such as automotive hands-free systems, smart home and loudspeaker devices, or web conference systems.
