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 …

Sound event localization and detection (SELD) involves identifying the direction-of-arrival
(DOA) and the event class. The SELD methods with a class-wise output format make the …

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 …

Recently, with the construction of smart city, the research on environmental sound
classification (ESC) has attracted the attention of academia and industry. The development …

The auditory brain circuits are biologically constructed to recand localize sounds by
encoding a combination of cues that help individuals interpret sounds. The development of …

Polyphonic sound event localization and detection (SELD) aims at detecting types of sound
events with corresponding temporal activities and spatial locations. In this paper, a trackwise …

Over the past few years, convolutional neural networks (CNNs) have been established as
the core architecture for audio classification and detection. In particular, a hybrid model that …

Sound event localization and detection (SELD) is a combined task of identifying the sound
event and its direction. Deep neural networks (DNNs) are utilized to associate them with the …

The goal of sound event localization and detection (SELD) is to detect the temporal
occurrence activity of a known set of sound events and locate them in the spatial space. We …