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 …

During the past three decades, the issue of processing spectral phase has been largely
neglected in speech applications. There is no doubt that the interest of speech processing …

Previous research on audio source separation based on deep neural networks (DNNs)
mainly focuses on estimating the magnitude spectrum of target sources and typically, phase …

Algorithmic latency in speech processing is dominated by the frame length used for Fourier
analysis, which in turn limits the achievable performance of magnitude-centric approaches …

In real-time applications, the aim of speech enhancement (SE) is to achieve optimal
performance while ensuring computational efficiency and near-instant outputs. Many deep …

In this paper, we propose a phase reconstruction framework, named Deep Griffin-Lim
Iteration (DeGLI). Phase reconstruction is a fundamental technique for improving the quality …

In earlier days, people used speech as a means of communication or the way a listener is
conveyed by voice or expression. But the idea of machine learning and various methods are …

Speech enhancement has been widely investigated for several decades, but by modifying
only the amplitude spectrum of a speech signal, ignoring the phase spectrum, which has …

This paper presents a two-stage online phase reconstruction framework using causal deep
neural networks (DNNs). Phase reconstruction is a task of recovering phase of the short-time …

Abstract This paper proposes Discrete Cosine Transform (DCT) based speech
enhancement algorithms. These algorithms utilize minimum mean square error (MMSE) …