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 …

We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural
networks. It consists in a streaming encoder-decoder architecture with quantized latent …

Recurrent Neural Networks (RNNs) have long been the dominant architecture in sequence-
to-sequence learning. RNNs, however, are inherently sequential models that do not allow …

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 …

We introduce a new approach for audio-visual speech separation. Given a video, the goal is
to extract the speech associated with a face in spite of simultaneous back-ground sounds …

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

We introduce Wavesplit, an end-to-end source separation system. From a single mixture, the
model infers a representation for each source and then estimates each source signal given …

Source separation for music is the task of isolating contributions, or stems, from different
instruments recorded individually and arranged together to form a song. Such components …

In recent years, rapid progress has been made on the problem of single-channel sound
separation using supervised training of deep neural networks. In such supervised …

We propose TF-GridNet for speech separation. The model is a novel deep neural network
(DNN) integrating full-and sub-band modeling in the time-frequency (TF) domain. It stacks …