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 …

Modern deep learning-based models have seen outstanding performance improvement with
speech enhancement tasks. The number of parameters of state-of-the-art models, however …

This paper presents recent advances in low-latency, single-channel, deep neural network-
based speech enhancement systems. The sources of latency and their acceptable values in …

Speech enhancement at extremely low signal-to-noise ratio (SNR) condition is a very
challenging problem and rarely investigated in previous works. This paper proposes a …

To achieve real-time single-channel speech enhancement, ie, enhancing with no or low
latency, this paper proposes a causal speech enhancement model with an attention …

This paper presents an improved subband neural network applied to joint speech denoising
and dereverberation for online single-channel scenarios. Preserving the advantages of …

The redundant convolutional encoder-decoder network has been proven useful in speech
enhancement tasks. This network can capture the localized time-frequency details of speech …

Speech enhancement approaches based on deep neural network have outperformed the
traditional signal processing methods. This paper presents a low-delay speech …

In this article a new neural network for speech enhancement is proposed where single-
channel noisy speech is processed in order to improve its intelligibility and quality. It is …

Capturing the contextual information in multi-scale is known to be beneficial for improving
the performance of DNN-based speech enhancement (SE) models. This paper proposes a …