Speech exhibits strong dependencies among its samples in both time and frequency
domains. In this paper, we propose a low-complexity composite model for speech …

Speech enhancement is a very important problem in various speech processing
applications. Recently, supervised speech enhancement using deep learning approaches to …

The data driven speech enhancement approaches using regression-based deep neural
network usually result in enormous number of model parameters, which increase the …

Speech enhancement (SE) is a critical aspect of various speech-processing applications.
Recent research in this field focuses on identifying effective ways to capture the long-term …

This paper proposes a deep learning-based densely connected Y-Net as an effective
network architecture for the fusion of time and frequency domain loss functions for speech …

Speech enhancement (SE) is an important method for improving speech quality and
intelligibility in noisy environments where received speech is severely distorted by noise. An …

In this study, a novel multi-objective speech enhancement algorithm is proposed. First, we
construct a deep learning architecture based on a stacked and temporal convolutional …

In this paper, we considered the problem of the speech enhancement similar to the real-
world environments where several complex noise sources simultaneously degrade the …

Over the past several decades, numerous speech enhancement techniques have been
proposed to improve the performance of modern communication devices in noisy …

In real-time applications, the aim of speech enhancement (SE) is to achieve optimal
performance while ensuring computational efficiency and near-instant outputs. Many deep …