Deep neural networks (DNN) techniques have become pervasive in domains such as
natural language processing and computer vision. They have achieved great success in …

Many deep learning-based speech enhancement algorithms are designed to minimize the
mean-square error (MSE) in some transform domain between a predicted and a target …

Though speech enhancement (SE) can be used to improve speech quality in noisy
environments, it may also cause distortions that degrade the performance of automatic …

Speech enhancement is the process of enhancing the clarity and intelligibility of speech
signals that have been degraded due to background noise. With the assistance of deep …

Speech signals reaching our ears are in general contaminated by the background noise
distortion which is detrimental to both speech quality and intelligibility. In this paper, we …

Perceptually-inspired objective functions such as the perceptual evaluation of speech
quality (PESQ), signal-to-distortion ratio (SDR), and short-time objective intelligibility (STOI) …

Neural network approaches to single-channel speech enhancement have received much
recent attention. In particular, mask-based architectures have achieved significant …

Data-driven speech intelligibility prediction has been slow totake off. Datasets of measured
speech intelligibility are scarce, and so current models are relatively small and rely on hand …

Many single-and multi-channel speech enhancement techniques, old and new, rely in one
way or another on estimates of the noise power spectral density (PSD). For example, the …

Objective: Although many speech enhancement (SE) algorithms have been proposed to
promote speech perception in hearing-impaired patients, the conventional SE approaches …