Monaural speech enhancement aims to remove background noise from an audio recording
containing speech in order to improve its clarity and intelligibility. Currently, the most …

Speech enhancement is an essential task for improving the quality and intelligibility of
speech signals corrupted by noise. Current deep neural network-based speech …

Speech signal enhancement achieves high-level performance in recent years using deep
learning techniques. However, the deep learning technique in the speech enhancement …

This paper proposes a novel fully convolutional neural network (FCN) called FLGCNN to
address the end-to-end speech enhancement in time domain. The proposed FLGCNN is …

Deep neural networks (DNN) have recently been shown to give state-of-the-art performance
in monaural speech enhancement. However in the DNN training process, the perceptual …

Speech enhancement aims to separate the speech and noise components of noisy speech.
It has greatly benefited from the convolutional encoder-decoder architecture. And designing …

Convolutional neural network (CNN) based methods, such as the convolutional encoder–
decoder network, offer state-of-the-art results in monaural speech enhancement. In the …

This paper proposes a novel cascade architecture to address the monaural speech
enhancement problem. We leverage three different domains of speech representation …

A key step to single channel speech enhancement is the orthogonal separation of speech
and noise. In this paper, a dual branch complex convolutional recurrent network (DBCCRN) …

Recently, deep neural networks (DNNs) were successfully introduced to the speech
enhancement area. Conventional DNN-based algorithms generally produce over-smoothed …