In this paper, we present an overview on the topic of noise reduction in the short-time Fourier
transform (STFT) domain. First, we briefly review the conventional literature in the single-and …

Reducing the interference noise in a monaural noisy speech signal has been a challenging
task for many years. Compared to traditional unsupervised speech enhancement methods …

Speech enhancement is challenging because of the diversity of background noise types.
Most of the existing methods are focused on modelling the speech rather than the noise. In …

With the recent research developments, deep learning models are powerful alternatives for
speech enhancement and recognition in many real-world applications. Although state-of-the …

A key stage in speech enhancement is noise estimation which usually requires prior models
for speech or noise or both. However, prior models can sometimes be difficult to obtain. In …

In this paper, we propose a Bayesian minimum mean squared error approach for the joint
estimation of the short-term predictor parameters of speech and noise, from the noisy …

The enhancement of speech degraded by real-world interferers is a highly relevant and
difficult task. Its importance arises from the multitude of practical applications, whereas the …

The performance of speech coding, speech recognition, and speech enhancement systems
that rely on the augmented Kalman filter (AKF) largely depend upon the accuracy of clean …

We present an approach for separating two speech signals when only one single recording
of their linear mixture is available. For this purpose, we derive a filter, which we call the soft …

Accurate modeling and estimation of speech and noise gains facilitate good performance of
speech enhancement methods using data-driven prior models. In this paper, we propose a …