Artificial Neural Networks (ANNs) were created inspired by the neural networks in the
human brain and have been widely applied in speech processing. The application areas of …

Deep complex U-Net structure and convolutional recurrent network (CRN) structure achieve
state-of-the-art performance for monaural speech enhancement. Both deep complex U-Net …

Temporal modulation processing is a promising technique for improving the intelligibility and
quality of speech in noise. We propose a speech enhancement algorithm that constructs the …

Many studies on deep learning-based speech enhancement (SE) utilizing the computational
auditory scene analysis method typically employs the ideal binary mask or the ideal ratio …

There are many deterministic mathematical operations (eg compression, clipping,
downsampling) that degrade speech quality considerably. In this paper we introduce a …

Exercised-induced cardiac fatigue (EICF) refers to an impermanent decline in systolic and
diastolic function caused by high-intensity and multi-frequency exercise. Long-term EICF …

Dual-path Transformer-style models have demonstrated significant effectiveness in speech
enhancement. However, extensive parameterization and computational complexity present …

Speech enhancement (SE) aims to improve speech quality and intelligibility by removing
acoustic corruption. While various SE models using audio-only (AO) based on deep learning …

Generative adversarial networks (GANs) have been increasingly used as feature mapping
functions in speech enhancement, in which the noisy speech features are transformed to the …

Nowadays, deep neural network has become the prime approach for enhancing speech
signals as it yields good results compared to the traditional methods. This paper describes …