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A survey of sound source localization with deep learning methods.

Pierre-Amaury Grumiaux1, Srđan Kitić2, Laurent Girin3

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This survey explores deep learning for sound source localization in challenging indoor environments. It maps neural network approaches, aiding researchers in selecting optimal methods for reverberant and noisy conditions.

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Area of Science:

  • Acoustics and Signal Processing
  • Artificial Intelligence and Machine Learning

Background:

  • Sound source localization (SSL) is crucial for applications like robotics and augmented reality.
  • Indoor environments present significant challenges due to reverberation and diffuse noise.
  • Existing deep learning methods for SSL require systematic organization and comparison.

Purpose of the Study:

  • To provide a comprehensive survey of deep learning-based sound source localization methods.
  • To focus specifically on SSL in challenging indoor acoustic conditions.
  • To organize and categorize existing literature based on key methodological aspects.

Main Methods:

  • Systematic literature review of deep learning for sound source localization.
  • Categorization based on neural network architecture, input features, and output strategies (classification/regression).
  • Analysis of data types for training/evaluation and model training strategies.

Main Results:

  • An extensive topography of neural network-based SSL literature is presented.
  • Methods are organized by network architecture, input features, output strategy, data, and training.
  • Summary tables facilitate quick searching for methods with specific characteristics.

Conclusions:

  • Deep learning offers powerful solutions for indoor sound source localization.
  • A structured overview aids researchers in navigating and advancing the field.
  • Further research can leverage this organized knowledge for improved SSL systems.