This article is a survey of deep learning methods for single and multiple sound source
localization, with a focus on sound source localization in indoor environments, where …

We present a new method for separating a mixed audio sequence, in which multiple voices
speak simultaneously. The new method employs gated neural networks that are trained to …

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 …

This paper presents a novel streaming automatic speech recognition (ASR) framework for
multi-talker overlap** speech captured by a distant microphone array with an arbitrary …

We propose multi-microphone complex spectral map**, a simple way of applying deep
learning for time-varying non-linear beamforming, for speaker separation in reverberant …

Abstract The Image Source Method (ISM) is one of the most employed techniques to
calculate acoustic Room Impulse Responses (RIRs), however, its computational complexity …

Standing upon the intersection of traditional beamformers and deep neural networks, we
propose a causal neural beamformer paradigm called Embedding and Beamforming, and …

The key advantage of using multiple microphones for speech enhancement is that spatial
filtering can be used to complement the tempo-spectral processing. In a traditional setting …

This work proposes a neural network to extensively exploit spatial information for
multichannel joint speech separation, denoising and dereverberation, named SpatialNet. In …

Recently, frequency domain all-neural beamforming methods have achieved remarkable
progress for multichannel speech separation. In parallel, the integration of time domain …