A survey of sound source localization with deep learning methods - Archive ouverte HAL Access content directly
Journal Articles Journal of the Acoustical Society of America Year : 2022

A survey of sound source localization with deep learning methods

Abstract

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 reverberation and diffuse noise are present. We provide an extensive topography of the neural network-based sound source localization literature in this context, organized according to the neural network architecture, the type of input features, the output strategy (classification or regression), the types of data used for model training and evaluation, and the model training strategy. Tables summarizing the literature survey are provided at the end of the paper, allowing a quick search of methods with a given set of target characteristics.
Fichier principal
Vignette du fichier
Grumiaux_et_al_JASA_2022_SSL.pdf (4.8 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

hal-03952034 , version 1 (31-01-2023)

Identifiers

Cite

Pierre-Amaury Grumiaux, Srđan Kitić, Laurent Girin, Alexandre Guérin. A survey of sound source localization with deep learning methods. Journal of the Acoustical Society of America, 2022, 152 (1), pp.107-151. ⟨10.1121/10.0011809⟩. ⟨hal-03952034⟩
3 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More