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

TL;DR

This paper presents a real-time method for mobile robots to localize and track multiple moving sound sources using beamforming and particle filtering, enhancing human-robot interaction.

Contribution

It introduces a robust, real-time sound source localization and tracking system for robots using an eight-microphone array, combining beamforming and particle filtering.

Findings

Successfully localizes and tracks multiple moving sources in real-time

Operates effectively over a 7-meter range

Enhances natural interaction capabilities for robots

Abstract

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.