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.

翻译：本文是对单一和多种可靠来源本地化的深层学习方法的调查,我们特别关心室内/家庭环境中的可靠来源本地化,因为有反响和扩散噪音,我们提供这方面基于神经的本地化文献的详尽地形图,按以下几个方面编排:神经网络结构、输入特征的类型、产出战略(分类或回归)、用于示范培训和评价的数据类型以及示范培训战略。这样,有兴趣的读者可以很容易地理解深层次基于学习的可靠来源本地化方法的广博全景。文件末尾提供了概述文献调查的表格,以快速搜索具有特定目标特征的方法。