Speech enhancement and speech separation are two related tasks, whose purpose is to
extract either one or more target speech signals, respectively, from a mixture of sounds …

Speech separation is the task of separating target speech from background interference.
Traditionally, speech separation is studied as a signal processing problem. A more recent …

Self-supervised learning (SSL) achieves great success in speech recognition, while limited
exploration has been attempted for other speech processing tasks. As speech signal …

Speech enhancement has benefited from the success of deep learning in terms of
intelligibility and perceptual quality. Conventional time-frequency (TF) domain methods …

Single-channel, speaker-independent speech separation methods have recently seen great
progress. However, the accuracy, latency, and computational cost of such methods remain …

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 …

We present a joint audio-visual model for isolating a single speech signal from a mixture of
sounds such as other speakers and background noise. Solving this task using only audio as …

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 …

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 …

Many real-world applications of speech enhancement, such as hearing aids and cochlear
implants, desire real-time processing, with no or low latency. In this paper, we propose a …