# Graph neural networks for sound source localization on distributed microphone networks

arXiv: https://arxiv.org/abs/2306.16081 · HTML: https://mlsanity.com/paper/2306.16081/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2023.

## Built on by (most cited first, 3 of 3)

- [wav2pos: Sound Source Localization using Masked Autoencoders](https://mlsanity.com/paper/2408.15771/) (2024)
- [SONNET: Enhancing Time Delay Estimation by Leveraging Simulated Audio](https://mlsanity.com/paper/2411.13179/) (2024)
- [Efficient and Microphone-Fault-Tolerant 3D Sound Source Localization](https://mlsanity.com/paper/2505.20961/) (2025)

## Abstract (arXiv)

Distributed Microphone Arrays (DMAs) present many challenges with respect to centralized microphone arrays. An important requirement of applications on these arrays is handling a variable number of input channels. We consider the use of Graph Neural Networks (GNNs) as a solution to this challenge. We present a localization method using the Relation Network GNN, which we show shares many similarities to classical signal processing algorithms for Sound Source Localization (SSL). We apply our method for the task of SSL and validate it experimentally using an unseen number of microphones. We test different feature extractors and show that our approach significantly outperforms classical baselines.