Search Results for author:
Found 5 papers, 0 papers with code
Efficient High-Performance Bark-Scale Neural Network for Residual Echo and Noise Suppression
In recent years, the introduction of neural networks (NNs) into the field of speech enhancement has brought significant improvements.
Efficient Acoustic Echo Suppression with Condition-Aware Training
The topic of deep acoustic echo control (DAEC) has seen many approaches with various model topologies in recent years.
Bandwidth-Scalable Fully Mask-Based Deep FCRN Acoustic Echo Cancellation and Postfiltering
Although today's speech communication systems support various bandwidths from narrowband to super-wideband and beyond, state-of-the art DNN methods for acoustic echo cancellation (AEC) are lacking modularity and bandwidth scalability.
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.
