Speech enhancement aims to reduce the noise and improve the quality and intelligibility of
noisy speech. Long short-term memory (LSTM) network frameworks have achieved great …

In recent years, deep learning models have been employed for speech enhancement. Most
of the existing methods based on deep learning use fully Convolutional Neural Network …

Because traditional single-channel speech enhancement algorithms are sensitive to the
environment and perform poorly, a speech enhancement algorithm based on attention …

A novel pre-training method is proposed to improve deep-neural-networks (DNN) and long-
short-termmemory (LSTM) performance, and reduce the local minimum problem for speech …

Recently, we proposed a novel progressive learning (PL) framework for deep neural
network (DNN) based speech enhancement to improve the performance in low signal-to …

Speech-in-noise perception is an important research problem in many real-world multimedia
applications. The noise-reduction methods contributed significantly; however rely on a priori …

Abstract Long Short-Term Memory (LSTM) recurrent neural network has proven effective in
modeling speech and has achieved outstanding performance in both speech enhancement …

Long Short-Term Memory (LSTM), a special kind of Recurrent Neural Network (RNN), is
capable of learning long-term dependencies. In this paper, a kind of speech enhancement …

Current deep learning-based speech enhancement methods focus on enhancing the time–
frequency representation of the signal. However, conventional methods can lead to speech …

Speech enhancement (SE) aims to improve the intelligibility and perceptual quality of
speech contaminated by noise signals through spectral or temporal changes. Deep learning …