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

Deep neural networks based methods dominate recent development in single channel
speech enhancement. In this paper, we propose a multi-scale feature recalibration …

In real-time applications, the aim of speech enhancement (SE) is to achieve optimal
performance while ensuring computational efficiency and near-instant outputs. Many deep …

Over the past ten years, deep learning has enabled significant advancements in the
improvement of noisy speech. Due to the short time stability of speech signal, previous …

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 …

While modern deep learning-based models have significantly outperformed traditional
methods in the area of speech enhancement, they often necessitate a lot of parameters and …

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

Speech enhancement is the task of taking a noisy speech input and producing an enhanced
speech output. In recent years, the need for speech enhancement has been increased due …

In this paper, we present a deep neural network architecture comprising of both
convolutional neural network (CNN) and recurrent neural network (RNN) layers for real-time …

Fully convolutional recurrent neural networks (FCRNs) have shown state-of-the-art
performance in single-channel speech enhancement. However, the number of parameters …