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 …

Most single channel audio source separation approaches produce separated sources
accompanied by interference from other sources and other distortions. To tackle this …

In linear regression, the coefficients are simple to estimate using the least squares method
with a known design matrix for the observed measurements. However, real-world …

Deep learning based speech separation usually uses a supervised algorithm to learn a
map** function from noisy features to separation targets. These separation targets, either …

Deep neural networks (DNNs) are often used to tackle the single channel source separation
(SCSS) problem by predicting time-frequency masks. The predicted masks are then used to …

We propose a multi‐objective joint model of non‐negative matrix factorization (NMF) and
deep neural network (DNN) with a new loss function for speech enhancement. The …

Speech separation algorithms are faced with a difficult task of producing high degree of
separation without containing unwanted artifacts. The time–frequency (T–F) masking …

This paper addresses the problem of acoustic noise reduction and speech enhancement in
new telecommunications systems by adaptive filtering algorithms. Recently, a particular …

In this paper, we present a new supervised speech enhancement approach based on the
cooperative structure of deep autoencoders (DAEs) as generative models and deep neural …

In this paper, we propose a jointly-optimized stacked-two-stage speech enhancement. In the
first stage, a convolutional recurrent network (CRN)-based masking is integrated with the …