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

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 …

Over the past 10 years, deep learning has enabled significant advancements in the
improvement of noisy speech. In an end-to-end speech enhancement, the deep neural …

Recent advancements in deep learning-based speech enhancement models have
extensively used attention mechanisms to achieve state-of-the-art methods by …

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 …

Speech is easily interfered by external environment in reality, which results in the loss of
important features. Deep learning has become a popular speech enhancement method …

Speech signal often gets corrupted by different noises like airport noise, station noise, and
street noise. These noises tend to degrade the quality of the speech signal, particularly in …

Because of their simple design structure, end-to-end deep learning (E2E-DL) models have
gained a lot of attention for speech enhancement. A number of DL models have achieved …

Deep learning (DL) networks have grown into powerful alternatives for speech
enhancement and have achieved excellent results by improving speech quality …

Deep learning based single-channel speech enhancement tries to train a neural network
model for the prediction of clean speech signal. There are a variety of popular network …