In this paper, we propose a transformer-based architecture, called two-stage transformer
neural network (TSTNN) for end-to-end speech denoising in the time domain. The proposed …

Recently, convolution-augmented transformer (Conformer) has achieved promising
performance in automatic speech recognition (ASR) and time-domain speech enhancement …

This paper proposes MP-SENet, a novel Speech Enhancement Network which directly
denoises Magnitude and Phase spectra in parallel. The proposed MP-SENet adopts a …

In this work, we further develop the conformer-based metric generative adversarial network
(CMGAN) model 1 for speech enhancement (SE) in the time-frequency (TF) domain. This …

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 …

With the development of deep neural networks, speech enhancement technology has been
vastly improved. However, commonly used speech enhancement approaches cannot fully …

In the field of speech enhancement, time domain methods have difficulties in achieving both
high performance and efficiency. Recently, dual-path models have been adopted to …

In speech enhancement, complex neural network has shown promising performance due to
their effectiveness in processing complex-valued spectrum. Most of the recent speech …

A number of recent advances in neural audio synthesis rely on up-sampling layers, which
can introduce undesired artifacts. In computer vision, upsampling artifacts have been …

Recent years have seen a significant amount of studies in the area of speech enhancement.
This review looks at several speech improvement methods as well as Deep Neural Network …