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 …

This report presents the dataset and baseline of Task 3 of the DCASE2021 Challenge on
Sound Event Localization and Detection (SELD). The dataset is based on emulation of real …

Sound event localization and detection (SELD) consists of two subtasks, which are sound
event detection and direction-of-arrival estimation. While sound event detection mainly relies …

Polyphonic sound event localization and detection (SELD) has many practical applications
in acoustic sensing and monitoring. However, the development of real-time SELD has been …

This report describes our systems submitted to the DCASE2021 challenge task 3: sound
event localization and detection (SELD) with directional interference. Our previous system …

The state-of-the-art deep neural networks trained on a large amount of data can better
diagnose cardiac arrhythmias than cardiologists. However, the requirement of the high …

Conventional microphone array methods for the characterization of sound sources that
require a focus-grid are, depending on the grid resolution, either computationally …

Recording and annotating real sound events for a sound event localization and detection
(SELD) task is time consuming, and data augmentation techniques are often favored when …

Polyphonic events are the main error source of audio event detection (AED) systems. In
deep-learning context, the most common approach to deal with event overlaps is to treat the …

The prevailing noise-resistant and reverberation-resistant localization algorithms primarily
emphasize separating and providing directional output for each speaker in multi-speaker …