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

中文翻译：

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无人机空中试听通过整合声源定位和数据关联来估计多个声源位置

无人机试听，无人机对多旋翼直升机（以下简称无人机）的听觉能力，已被开发用于通过补偿由于黑暗和遮挡导致的视觉能力弱点来改善现实世界的任务，例如搜救任务。当前大多数机器人试听的实现都集中在单个声源上。本文重点研究从配备麦克风阵列的无人机捕获的声学信号中估计多个声源位置。由于无人机周围的转子和气流噪声等自我噪声，对声源位置的估计是模糊的并且容易出错。特别是在多个声源的情况下，定位信息和声源之间的数据关联对于这种估计的性能至关重要。为了应对数据关联的不确定性，我们扩展了全球最近邻 (GNN) 以利用声源特征 (GNN-c)，因为无人机试听需要实时或最少的延迟。由此产生的系统表明它可以以大约 3 m 的精度估计多个声源位置。图形概要