Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …

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 …

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 propose a convolutional recurrent neural network for joint sound event
localization and detection (SELD) of multiple overlap** 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 …

Although advances in close-talk speech recognition have resulted in relatively low error
rates, the recognition performance in far-field environments is still limited due to low signal …

Sound event detection (SED) and localization refer to recognizing sound events and
estimating their spatial and temporal locations. Using neural networks has become the …

Multi-source localization is an important and challenging technique for multi-talker
conversation analysis. This paper proposes a novel supervised learning method using deep …