Drone audition listening from the sky estimates multiple sound source positions by integrating sound source localization and data association. (2nd June 2020)

• Record Type: Journal Article
• Authors: Wakabayashi, Mizuho; Okuno, Hiroshi G.; Kumon, Makoto
• Is Part Of: Advanced robotics. Volume 34:Number 11(2020)
• Page Start: 744
• Page End: 755
• Publication Date: 2020-06-02
• Subjects: Drone audition -- robot audition -- sound source localization -- sound source position estimation -- computational auditory scene analysis -- data association; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2020.1757506
• Languages: English
• ISSNs: 0169-1864

Abstract

Abstract : Drone audition, drone's auditory capabilities for a multi-rotor helicopter (hereinafter, drone ), has been developed to improve real-world tasks, e.g. search-and-rescue tasks, by compensating for the weakness of visual capabilities due to darkness and occlusion. Most of current implementations of robot audition focus on a single sound source. This paper focuses on the estimation of multiple sound source positions from acoustic signals captured by a drone equipped with a microphone array. Due to ego-noise such as rotor and airflow noise around the drone, the estimation of the sound source position is obscured and prone to error. In particular, in case of multiple sound sources, data association between localization information and sound sources is critical to the performance of such estimation. To cope with uncertainty in data association, we extend Global Nearest Neighbor (GNN) to exploit sound source features (GNN-c) because drone audition needs real-time or least latency. The resulting system demonstrates that it can estimate multiple sound source positions with an accuracy of about 3 m. GRAPHICAL ABSTRACT: UF0001