Recently, algorithms based on deep neural networks have led to a significant speech
enhancement performance improvement in terms of speech quality and intelligibility both for …

This paper focuses on a regression-based deep neural network (DNN) approach for single-
channel speech enhancement. While DNN can lead to improved speech quality compared …

In mobile speech communication, the quality and intelligibility of the received speech can be
severely degraded by background noise if the far-end talker is in an adverse acoustic …

Single-channel speech enhancement has made great progress with the development of
deep learning. Recently, some researchers predict the real and imaginary parts of the output …

Current supervised speech enhancement methods based on deep learning typically utilize
amplitude-based loss functions for optimization, such as Mean Absolute Error (MAE) or …

This paper proposes a neural network based system for multichannel speech enhancement
and dereverberation. Speech recorded indoors by a far field microphone, is invariably …

Deep learning has led to unprecedented advances in speech enhancement. However, deep
neural networks (DNNs) typically require large amount of computation, memory, signal …

Abstract In recent years, Temporal Convolutional Networks (TCNs) have driven significant
progress in single-channel noisy speech enhancement. However, TCN-based systems still …

In recent years, supervised approaches using deep neural networks (DNNs) have become
the mainstream for speech enhancement. It has been established that DNNs generalize well …

In recent decades, neural network based methods have significantly improved the
performance of speech enhancement. Most of them estimate time-frequency (TF) …