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 …

With the advent of deep learning, research on noise-robust automatic speech recognition
(ASR) has progressed rapidly. However, ASR performance in noisy conditions of single …

Recurrent neural networks (RNNs) are well-suited for sequential tasks such as speech
enhancement (SE). However, their performance comes with high-computational complexity …

One of the most important methods of predicting the future is through past events and data
repeated over time, as time series are those data indexed using time sequentially on data …

Speech Separation is among the propelled advances for a wide range of uses in different
sectors, where detachment from the Blind Source Separation Signal is a troublesome task …

In recent years, speech processing algorithms have seen tremendous progress primarily
due to the deep learning renaissance. This is especially true for speech separation where …

Analysis of child-centred daylong naturalist audio recordings has become a de-facto
research protocol in the scientific study of child language development. The researchers are …

Deep neural networks have revolutionized many different areas, including speech
enhancement, speech recognition, and speech separation that are relevant for hearing …

The development of speech-enabled mobile applications has greatly improved human-
computer interaction in recent years. These applications are flexible and convenient for …

The article encompasses microphone array speech processing using neural networks.
Noisy microphone array, which consists of 12 elements, is simulated from clean and noise …