A survey of sound source localization with deep learning methods
École Centrale de Nantes · Centre National de la Recherche Scientifique · +6 more institutions
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
- FWCI
- 43.68
- Percentile
- 100%
- References
- 381
Authors
4- PGPierre-Amaury GrumiauxCorresponding
École Centrale de Nantes, Centre National de la Recherche Scientifique, Laboratoire des Sciences du Numérique de Nantes, Nantes Université
- SKSrđan Kitić
Orange (France)
- LGLaurent Girin
Institut polytechnique de Grenoble, GIPSA-Lab, Université Grenoble Alpes
- AGAlexandre Guérin
Orange (France)
Topics & keywords
- Computer science
- Reverberation
- Deep learning
- Artificial neural network
- Artificial intelligence
- Context (archaeology)
- Set (abstract data type)
- Panorama
- Quality Education