Deep learning for environmentally robust speech recognition: An overview of recent developments
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 …
research topic for automatic speech recognition but still remains an important challenge …
A review of the state of the art and future challenges of deep learning-based beamforming
H Al Kassir, ZD Zaharis, PI Lazaridis… - IEEE …, 2022 - ieeexplore.ieee.org
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 …
(AI) applications on the broad field of beamforming. Hence, a multitude of AI-oriented …
[HTML][HTML] A survey of sound source localization with deep learning methods
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 …
localization, with a focus on sound source localization in indoor environments, where …
[HTML][HTML] Machine learning in acoustics: Theory and applications
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 …
communications to ocean and Earth science. We survey the recent advances and …
Sound event localization and detection of overlapping sources using convolutional recurrent neural networks
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 …
localization and detection (SELD) of multiple overlapping sound events in three-dimensional …
Direction-of-arrival estimation based on deep neural networks with robustness to array imperfections
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 …
precision direction-of-arrival (DOA) estimation methods. Machine learning-based methods …
Multi-speaker DOA estimation using deep convolutional networks trained with noise signals
S Chakrabarty, EAP Habets - IEEE Journal of Selected Topics …, 2019 - ieeexplore.ieee.org
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 …
adapted to different acoustic conditions via training and have been shown to be robust to …
Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network
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 …
multiple sound sources. The proposed stacked convolutional and recurrent neural network …
Broadband DOA estimation using convolutional neural networks trained with noise signals
S Chakrabarty, EAP Habets - … of Signal Processing to Audio and …, 2017 - ieeexplore.ieee.org
A convolution neural network (CNN) based classification method for broadband DOA
estimation is proposed, where the phase component of the short-time Fourier transform …
estimation is proposed, where the phase component of the short-time Fourier transform …
Multichannel signal processing with deep neural networks for automatic speech recognition
Multichannel automatic speech recognition (ASR) systems commonly separate speech
enhancement, including localization, beamforming, and postfiltering, from acoustic …
enhancement, including localization, beamforming, and postfiltering, from acoustic …