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 …

In recent years, with the rapid development of artificial intelligence, image caption has
gradually attracted the attention of many researchers in the field of artificial intelligence and …

In this work, we build upon our previous publication and use diffusion-based generative
models for speech enhancement. We present a detailed overview of the diffusion process …

Speech enhancement is a critical component of many user-oriented audio applications, yet
current systems still suffer from distorted and unnatural outputs. While generative models …

The discrepancy between the cost function used for training a speech enhancement model
and human auditory perception usually makes the quality of enhanced speech …

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 …

Time-frequency (TF) domain masking is a mainstream approach for single-channel speech
enhancement. Recently, focuses have been put to phase prediction in addition to amplitude …

Current speech enhancement techniques operate on the spectral domain and/or exploit
some higher-level feature. The majority of them tackle a limited number of noise conditions …

Adversarial loss in a conditional generative adversarial network (GAN) is not designed to
directly optimize evaluation metrics of a target task, and thus, may not always guide the …

Diffusion models have shown a great ability at bridging the performance gap between
predictive and generative approaches for speech enhancement. We have shown that they …