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 …

Deep neural networks (DNN) techniques have become pervasive in domains such as
natural language processing and computer vision. They have achieved great success in …

The denoising of mechanical system is always an indispensable process in sensor signal
analysis. It directly affects the result of subsequent tool state monitoring and identification …

Most deep learning-based speech enhancement methods operate directly on time-
frequency representations or learned features without making use of the model of speech …

Bone-conducted speech is not susceptible to background noise but suffers from poor
speech quality and intelligibility due to the limited bandwidth. This paper proposes a two …

Cycle-consistent generative adversarial networks (CycleGAN) have shown their promising
performance for speech enhancement (SE), while one intractable shortcoming of these …

Speech quality estimation has recently undergone a paradigm shift from human-hearing
expert designs to machine-learning models. However, current models rely mainly on …

Deep neural network-based speech enhancement systems have achieved promising
results. However, the state-of-the-art (SOTA) models usually have too many parameters and …

Audio-visual speech enhancement aims to extract clean speech from a noisy environment
by leveraging not only the audio itself but also the target speaker's lip movements. This …

Speech enhancement refers to suppressing the background noise to improve the perceptual
quality and intelligibility of the observed noisy speech. Recently, speech enhancement …