# Robust Localization and Tracking of Simultaneous Moving Sound Sources Using Beamforming and Particle Filtering

arXiv: https://arxiv.org/abs/1602.08139 · HTML: https://mlsanity.com/paper/1602.08139/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2016.

## Built on by (most cited first, 2 of 2)

- [A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods](https://mlsanity.com/paper/2507.01143/) (2025)
- [Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and Applications](https://mlsanity.com/paper/2306.05655/) (2023)

## Abstract (arXiv)

Mobile robots in real-life settings would benefit from being able to localize and track sound sources. Such a capability can help localizing a person or an interesting event in the environment, and also provides enhanced processing for other capabilities such as speech recognition. To give this capability to a robot, the challenge is not only to localize simultaneous sound sources, but to track them over time. In this paper we propose a robust sound source localization and tracking method using an array of eight microphones. The method is based on a frequency-domain implementation of a steered beamformer along with a particle filter-based tracking algorithm. Results show that a mobile robot can localize and track in real-time multiple moving sources of different types over a range of 7 meters. These new capabilities allow a mobile robot to interact using more natural means with people in real life settings.