A joint neural network decoder and denoiser scheme demonstrated superior performance
compared to individual modules. However, there is still a limitation that the existing …

The noises embedded in signals will degrade the signal processing quality. Traditional
denoising algorithms might not work in practical systems since the statistical characteristics …

The paper proposes an Adaptive Stacked Denoising Autoencoder (ASDA) to overcome the
limitations of Stacked Denoising Autoencoder (SDA)[6] in which noise level is kept fixed …

This letter introduces a new denoiser that modifies the structure of denoising autoencoder
(DAE), namely noise learning based DAE (nlDAE). The proposed nlDAE learns the noise of …

The simultaneous denoising and dereverberation for single-channel mixture speech under
the complicated acoustic environment is considered to be a challengeable task. In this …

Deep neural networks (DNNs) with noisy weights, which we refer to as noisy neural
networks (NoisyNNs), arise from the training and inference of DNNs in the presence of …

We propose a novel image denoising method based on a convolutional neural network
(CNN), which uses the separable convolution and the gradient prior to reduce the …

In modern communication systems, multiple types of error-correcting codes can be utilized
for different transmission scenarios. Therefore, the receiver should include the decoder …

Deep learning based signal processing technology has been applied in communication
systems for enhancing information transmission performances, whose learning capability …

Stacked sparse denoising auto-encoders (SSDAs) have recently been shown to be
successful at removing noise from corrupted images. However, like most denoising …