Abstract Deep Nonnegative Matrix Factorization (Deep NMF) is an effective strategy for
feature extraction in recent years. By decomposing the matrix recurrently on account of the …

Deep neural network (DNN) have become a popular means of separating a target source
from a mixed signal. Most of DNN-based methods modify only the magnitude spectrum of …

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 …

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 …

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 …

Recently, deep neural network (DNN)-based speech enhancement has shown considerable
success, and map**-based and masking-based are the two most commonly used …

The advent of learning-based methods in speech enhancement has revived the need for
robust and reliable training features that can compactly represent speech signals while …

Speech enhancement plays an important role in robust speech processing. Deep learning
has become a new trend towards solving speech enhancement problems. The input feature …

We introduce a noise-adaptive feed-forward deep neural network (DNN) for single-channel
speech enhancement. The goal is to better exploit individual noise characteristics while …