# The LOCATA Challenge: Acoustic Source Localization and Tracking

arXiv: https://arxiv.org/abs/1909.01008 · HTML: https://mlsanity.com/paper/1909.01008/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2019.

## Built on by (most cited first, 17 of 17)

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [BAT: Learning to Reason about Spatial Sounds with Large Language Models](https://mlsanity.com/paper/2402.01591/) (2024)
- [Exploiting Attention-based Sequence-to-Sequence Architectures for Sound Event Localization](https://mlsanity.com/paper/2103.00417/) (2021)
- [Semi-supervised source localization in reverberant environments with deep generative modeling](https://mlsanity.com/paper/2101.10636/) (2021)
- [Speaker Embeddings to Improve Tracking of Intermittent and Moving Speakers](https://mlsanity.com/paper/2506.19875/) (2025)
- [SonicBench: Dissecting the Physical Perception Bottleneck in Large Audio Language Models](https://mlsanity.com/paper/2601.11039/) (2026)
- [Locate This, Not That: Class-Conditioned Sound Event DOA Estimation](https://mlsanity.com/paper/2203.04197/) (2022)
- [LOCUS: LOcalization with Channel Uncertainty and Sporadic Energy](https://mlsanity.com/paper/2302.09409/) (2023)
- [A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods](https://mlsanity.com/paper/2507.01143/) (2025)
- [Autoregressive Guidance of Deep Spatially Selective Filters using Bayesian Tracking for Efficient Extraction of Moving Speakers](https://mlsanity.com/paper/2603.23723/) (2026)
- [Diarisation using location tracking with agglomerative clustering](https://mlsanity.com/paper/2109.10598/) (2021)
- [Joint speaker diarisation and tracking in switching state-space model](https://mlsanity.com/paper/2109.11140/) (2021)
- [An experiment on an automated literature survey of data-driven speech enhancement methods](https://mlsanity.com/paper/2310.06260/) (2023)
- [Audio Simulation for Sound Source Localization in Virtual Evironment](https://mlsanity.com/paper/2404.01611/) (2024)
- [Towards Low-Latency Tracking of Multiple Speakers With Short-Context Speaker Embeddings](https://mlsanity.com/paper/2508.14115/) (2025)
- [Supervising Sound Localization by In-the-wild Egomotion](https://mlsanity.com/paper/2610.01388/) (2026)
- [SonicWorld: Source-Centric Latent Dynamics for Spatial Audio Reasoning](https://mlsanity.com/paper/2610.17131/) (2026)

## Abstract (arXiv)

The ability to localize and track acoustic events is a fundamental prerequisite for equipping machines with the ability to be aware of and engage with humans in their surrounding environment. However, in realistic scenarios, audio signals are adversely affected by reverberation, noise, interference, and periods of speech inactivity. In dynamic scenarios, where the sources and microphone platforms may be moving, the signals are additionally affected by variations in the source-sensor geometries. In practice, approaches to sound source localization and tracking are often impeded by missing estimates of active sources, estimation errors, as well as false estimates. The aim of the LOCAlization and TrAcking (LOCATA) Challenge is an open-access framework for the objective evaluation and benchmarking of broad classes of algorithms for sound source localization and tracking. This article provides a review of relevant localization and tracking algorithms and, within the context of the existing literature, a detailed evaluation and dissemination of the LOCATA submissions. The evaluation highlights achievements in the field, open challenges, and identifies potential future directions.