Frequency-domain monaural speech enhancement has been extensively studied for over
60 years, and a great number of methods have been proposed and applied to many …

Given the recent surge in developments of deep learning, this paper provides a review of the
state-of-the-art deep learning techniques for audio signal processing. Speech, music, and …

This work proposes a fully convolutional neural network (CNN) for real-time speech
enhancement in the time domain. The proposed CNN is an encoder-decoder based …

Phase is important for perceptual quality of speech. However, it seems intractable to directly
estimate phase spectra through supervised learning due to their lack of spectrotemporal …

This paper proposes a new learning mechanism for a fully convolutional neural network
(CNN) to address speech enhancement in the time domain. The CNN takes as input the time …

Many deep learning-based speech enhancement algorithms are designed to minimize the
mean-square error (MSE) in some transform domain between a predicted and a target …

Speech enhancement in the time domain is becoming increasingly popular in recent years,
due to its capability to jointly enhance both the magnitude and the phase of speech. In this …

Noisy situations cause huge problems for the hearing-impaired, as hearing aids often make
speech more audible but do not always restore intelligibility. In noisy settings, humans …

In recent years, deep networks have led to dramatic improvements in speech enhancement
by framing it as a data-driven pattern recognition problem. In many modern enhancement …

In this work, we propose a fully convolutional neural network for real-time speech
enhancement in the time domain. The proposed network is an encoder-decoder based …