Robust Localization and Tracking of Simultaneous Moving Sound Sources Using Beamforming and Particle Filtering
This enables mobile robots to interact more naturally with people in real-life settings by enhancing capabilities like person localization and speech recognition.
The paper tackled the problem of localizing and tracking multiple moving sound sources in real-time for mobile robots, achieving the ability to track sources up to 7 meters away.
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.