# Efficient and Microphone-Fault-Tolerant 3D Sound Source Localization

arXiv: https://arxiv.org/abs/2505.20961 · HTML: https://mlsanity.com/paper/2505.20961/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [Robust Sound Source Tracking Using SRP-PHAT and 3D Convolutional Neural Networks](https://mlsanity.com/paper/2006.09006/) (2020)
- [SyncNet: correlating objective for time delay estimation in audio signals](https://mlsanity.com/paper/2203.14639/) (2022)
- [FN-SSL: Full-Band and Narrow-Band Fusion for Sound Source Localization](https://mlsanity.com/paper/2305.19610/) (2023)
- [Extending GCC-PHAT using Shift Equivariant Neural Networks](https://mlsanity.com/paper/2208.04654/) (2022)
- [End-to-end Two-dimensional Sound Source Localization With Ad-hoc Microphone Arrays](https://mlsanity.com/paper/2210.08484/) (2022)
- [Graph neural networks for sound source localization on distributed microphone networks](https://mlsanity.com/paper/2306.16081/) (2023)
- [Dual input neural networks for positional sound source localization](https://mlsanity.com/paper/2308.04169/) (2023)
- [SOLVIT:A Reference-Free Source Localization Technique using Majorization Minimization](https://mlsanity.com/paper/1905.04500/) (2019)
- [The LuViRA Dataset: Synchronized Vision, Radio, and Audio Sensors for Indoor Localization](https://mlsanity.com/paper/2302.05309/) (2023)
- [wav2pos: Sound Source Localization using Masked Autoencoders](https://mlsanity.com/paper/2408.15771/) (2024)

## Abstract (arXiv)

Sound source localization (SSL) is a critical technology for determining the position of sound sources in complex environments. However, existing methods face challenges such as high computational costs and precise calibration requirements, limiting their deployment in dynamic or resource-constrained environments. This paper introduces a novel 3D SSL framework, which uses sparse cross-attention, pretraining, and adaptive signal coherence metrics, to achieve accurate and computationally efficient localization with fewer input microphones. The framework also supports operational microphones at unknown positions: their recordings remain available and are used to estimate both source and microphone positions. Preliminary experiments demonstrate its scalability for multi-source localization without requiring additional hardware. This work advances SSL by balancing the model's performance and efficiency and improving its robustness for real-world scenarios.