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 …

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

In this paper, we propose a novel method which includes autoencoder and deep neural
networks (DNNs) in a hierarchal structure for speech enhancement. In this method, at first …

We propose a multi-objective framework to learn both secondary targets not directly related
to the intended task of speech enhancement (SE) and the primary target of the clean log …

In this study, we propose a novel deep neural network (DNN) architecture for speech
enhancement (SE) via a multiobjective learning and ensembling (MOLE) framework to …

Recently, deep neural networks (DNNs) have become the mainstream strategy for speech
enhancement task because it can achieve the higher speech quality and intelligibility than …

Single-channel speech enhancement with deep neural networks (DNNs) has shown
promising performance and is thus intensively being studied. In this paper, instead of …

We propose a multi-target, signal-to-noise-ratio (SNR)-progressive learning (SNR-PL)
framework for regression based speech enhancement (SE). At low SNR levels, it is often not …

Nowadays, due to the application of deep neural network (DNNS), speech enhancement
(SE) technology has been significantly developed. However, most of current approaches …

Deep learning-based models have greatly advanced the performance of speech
enhancement (SE) systems. However, two problems remain unsolved, which are closely …