Eliminating the negative effect of non-stationary environmental noise is a long-standing
research topic for automatic speech recognition but still remains an important challenge …

The key objective of this paper is to explore the recent state-of-the-art artificial intelligence
(AI) applications on the broad field of beamforming. Hence, a multitude of AI-oriented …

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 …

Acoustic data provide scientific and engineering insights in fields ranging from biology and
communications to ocean and Earth science. We survey the recent advances and …

In this paper, we propose a convolutional recurrent neural network for joint sound event
localization and detection (SELD) of multiple overlapping sound events in three-dimensional …

Lacking of adaptation to various array imperfections is an open problem for most high-
precision direction-of-arrival (DOA) estimation methods. Machine learning-based methods …

Supervised learning-based methods for source localization, being data driven, can be
adapted to different acoustic conditions via training and have been shown to be robust to …

This paper proposes a deep neural network for estimating the directions of arrival (DOA) of
multiple sound sources. The proposed stacked convolutional and recurrent neural network …

A convolution neural network (CNN) based classification method for broadband DOA
estimation is proposed, where the phase component of the short-time Fourier transform …

Multichannel automatic speech recognition (ASR) systems commonly separate speech
enhancement, including localization, beamforming, and postfiltering, from acoustic …