Currently, most of the speech enhancement methods can't address the performance
degradation problem caused by low signal-to-noise ratios (SNR) and non-stationary noises …

Major breakthroughs have been made in speech enhancement with the introduction of deep
learning. However, the noise reduction performance under the lower signal-to-noise ratio …

The data driven speech enhancement approaches using regression-based deep neural
network usually result in enormous number of model parameters, which increase the …

The speech enhancement problem has made great progress with the development of deep
learning, however, the current methods rarely consider the weight distribution of the …

In recent years, the attention transformer model has been widely used in the field of speech
enhancement. With the introduction of a convolutionally enhanced transformer (Conformer) …

In order to solve the problem that the popular monaural speech enhancement models that
based on encoder-decoder do not make full use of full-scale features, a full-scale feature …

The purpose of speech enhancement is to extract useful speech signal from noisy speech.
The performance of speech enhancement has been improved greatly in recent years with …

In this paper, we incorporate the attention gates (AG) into the convolutional recurrent neural
network (CRNN) to perform speech enhancement. The attention gates, which enhance …

In real acoustic environment, speech enhancement is an arduous task to improve the quality
and intelligibility of speech interfered by background noise and reverberation. Over the past …

Recurrent neural networks (RNNs) have shown significant improvements in recent years for
speech enhancement. However, the model complexity and inference time cost of RNNs are …