Search Results for author:
Found 7 papers, 3 papers with code
ICASSP 2023 Speech Signal Improvement Challenge
The ICASSP 2023 Speech Signal Improvement Challenge is intended to stimulate research in the area of improving the speech signal quality in communication systems.
Guided Unsupervised Learning by Subaperture Decomposition for Ocean SAR Image Retrieval
Spaceborne synthetic aperture radar (SAR) can provide accurate images of the ocean surface roughness day-or-night in nearly all weather conditions, being an unique asset for many geophysical applications.
Deep model with built-in cross-attention alignment for acoustic echo cancellation
Since acoustic echo is one of the major sources of poor audio quality, a wide variety of deep models have been proposed.
Automotive Radar Interference Mitigation with Unfolded Robust PCA based on Residual Overcomplete Auto-Encoder Blocks
In autonomous driving, radar systems play an important role in detecting targets such as other vehicles on the road.
Estimating the Magnitude and Phase of Automotive Radar Signals under Multiple Interference Sources with Fully Convolutional Networks
In order to train our network in a real-world scenario, we introduce a new data set of realistic automotive radar signals with multiple targets and multiple interferers.
Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation
Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to objectively compare their future work in this domain.
Signal Processing
Are you wearing a mask? Improving mask detection from speech using augmentation by cycle-consistent GANs
Original and translated utterances are converted into spectrograms which are provided as input to a set of ResNet neural networks with various depths.
