Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

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 …

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

In speech enhancement and source separation, signal-to-noise ratio is a ubiquitous
objective measure of denoising/separation quality. A decade ago, the BSS_eval toolkit was …

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 …

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 …

Phase is important for perceptual quality of speech. However, it seems intractable to directly
estimate phase spectra through supervised learning due to their lack of spectrotemporal …

In this paper, we propose the utterance-level permutation invariant training (uPIT) technique.
uPIT is a practically applicable, end-to-end, deep-learning-based solution for speaker …

We propose a novel deep learning training criterion, named permutation invariant training
(PIT), for speaker independent multi-talker speech separation, commonly known as the …