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

Electroencephalogram (EEG) is often corrupted with artifacts originating from sources such
as eyes and muscles. Hybrid artifact removal methods often require human intervention for …

With recent research advancements, deep learning models are be-coming attractive and
powerful choices for speech enhancement in real-time applications. While state-of-the-art …

Deep learning-based speech enhancement for real-time applications recently made large
advancements. Due to the lack of a tractable perceptual optimization target, many myths …

Speech enhancement using neural networks is recently receiving large attention in research
and being integrated in commercial devices and applications. In this work, we investigate …

We explore a variety of neural networks configurations for one-and two-channel
spectrogram-mask-based speech enhancement. Our best model improves on previous state …

Time-domain audio separation networks based on dilated temporal convolutions have
recently been shown to perform very well compared to methods that are based on a time …

The recently-proposed deep clustering algorithm introduced significant advances in
monaural speaker-independent multi-speaker speech separation. Deep clustering operates …

Big data has been transformed into knowledge by information systems to add value in
businesses. Enterprises relying on it benefit from risk management to a certain extent. The …

Frame-online speech enhancement systems in the short-time Fourier transform (STFT)
domain usually have an algorithmic latency equal to the window size due to the use of …