A Survey of Sound Source Localization with Deep Learning Methods

Autor:	Grumiaux, Pierre-Amaury, Kitić, Srđan, Girin, Laurent, Guérin, Alexandre
Rok vydání:	2021
Předmět:	Computer Science - Sound Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
DOI:	10.1121/10.0011809
Popis:	This article is a survey on deep learning methods for single and multiple sound source localization. We are particularly interested in sound source localization in indoor/domestic environment, where reverberation and diffuse noise are present. We provide an exhaustive topography of the neural-based localization literature in this context, organized according to several aspects: 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. This way, an interested reader can easily comprehend the vast panorama of the deep learning-based sound source localization methods. Tables summarizing the literature survey are provided at the end of the paper for a quick search of methods with a given set of target characteristics. Comment: Accepted for publication in The Journal of the Acoustical Society of America
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.03465 Zobrazit plný text záznamu View this record from Arxiv