The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

Large-scale generative models such as GPT and DALL-E have revolutionized the research
community. These models not only generate high fidelity outputs, but are also generalists …

Speech enhancement has benefited from the success of deep learning in terms of
intelligibility and perceptual quality. Conventional time-frequency (TF) domain methods …

We present a causal speech enhancement model working on the raw waveform that runs in
real-time on a laptop CPU. The proposed model is based on an encoder-decoder …

Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

Single-channel, speaker-independent speech separation methods have recently seen great
progress. However, the accuracy, latency, and computational cost of such methods remain …

Given the recent surge in developments of deep learning, this paper provides a review of the
state-of-the-art deep learning techniques for audio signal processing. Speech, music, and …

In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for
the deep neural network (DNN) based vector-to-vector regression. The goal of this work is …

The INTERSPEECH 2020 Deep Noise Suppression (DNS) Challenge is intended to
promote collaborative research in real-time single-channel Speech Enhancement aimed to …