A Survey of Sound Source Localization with Deep Learning Methods

TL;DR

This survey comprehensively reviews deep learning techniques for sound source localization in indoor environments, highlighting architectures, input features, training data, and evaluation strategies to aid researchers in understanding current methods.

Contribution

It provides an exhaustive organization and summary of neural network-based sound source localization methods, including detailed tables for quick reference.

Findings

Extensive categorization of neural network architectures used in localization.

Analysis of input features and output strategies across methods.

Summary of datasets and training strategies employed in the literature.

Abstract

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.