We compare the run-time complexity of recent deep neural network (DNN) and non-DNN
based monaural speech enhancement algorithms. Specifically, we consider fully connected …

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 …

Monaural speech enhancement is a key yet challenging problem for many important real
world applications. Recently, deep neural networks (DNNs)-based speech enhancement …

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

Despite multiple efforts made towards adopting complex-valued deep neural networks
(DNNs), it remains an open question whether complex-valued DNNs are generally more …

In this paper we propose a Deep Neural Network (D NN) based Speech Enhancement (SE)
system that is designed to maximize an approximation of the Short-Time Objective …

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

Recently, progressive learning has shown its capacity to improve speech quality and speech
intelligibility when it is combined with deep neural network (DNN) and long short-term …

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 …