A survey of sound source localization with deep learning methods

École Centrale de Nantes · Centre National de la Recherche Scientifique · +6 more institutions

PubMed
Indexed inarxivcrossrefpubmed

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.

Citation impact

324
total citations
FWCI
43.68
Percentile
100%
References
381
Citations per year

Authors

4

Topics & keywords

Keywords
  • Computer science
  • Reverberation
  • Deep learning
  • Artificial neural network
  • Artificial intelligence
  • Context (archaeology)
  • Set (abstract data type)
  • Panorama
UN Sustainable Development Goals
  • Quality Education
No related works found for this paper.

Funding