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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >On the Use of the AIRA-UAS Corpus to Evaluate Audio Processing Algorithms in Unmanned Aerial Systems
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On the Use of the AIRA-UAS Corpus to Evaluate Audio Processing Algorithms in Unmanned Aerial Systems

机译：关于使用AIRA-UAS语料库评估无人机系统中的音频处理算法

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摘要

Audio analysis over an Unmanned Aerial Systems (UAS) is of interest it is an essential step for on-board sound source localization and separation. This could be useful for search & rescue operations, as well as for detection of unauthorized drone operations. In this paper, an analysis of the previously introduced Acoustic Interactions for Robot Audition (AIRA)-UAS corpus is presented, which is a set of recordings produced by the ego-noise of a drone performing different aerial maneuvers and by other drones flying nearby. It was found that the recordings have a very low Signal-to-Noise Ratio (SNR), that the noise is dynamic depending of the drone’s movements, and that their noise signatures are highly correlated. Three popular filtering techniques were evaluated in this work in terms of noise reduction and signature extraction, which are: Berouti’s Non-Linear Noise Subtraction, Adaptive Quantile Based Noise Estimation, and Improved Minima Controlled Recursive Averaging. Although there was moderate success in noise reduction, no filter was able to keep intact the signature of the drone flying in parallel. These results are evidence of the challenge in audio processing over drones, implying that this is a field prime for further research.
机译：有趣的是，对无人机系统（UAS）进行音频分析是对机载声源定位和分离必不可少的步骤。这对于搜索和营救操作以及检测未经授权的无人机操作可能很有用。在本文中，对先前介绍的机器人试听声交互（AIRA）-UAS语料库进行了分析，这是一组由执行不同空中操作的无人机的自我噪声和附近飞行的其他无人机产生的录音。结果发现，录音的信噪比（SNR）极低，噪声随无人机的运动而变化，并且它们的噪声特征高度相关。在降噪和特征提取方面，对三种流行的滤波技术进行了评估，它们是：Berouti的非线性噪声减法，基于自适应分位数的噪声估计和改进的最小控制递归平均。尽管在降噪方面取得了一定的成功，但没有过滤器能够保持并行飞行的无人机的特征。这些结果证明了无人机进行音频处理所面临的挑战，这表明这是需要进一步研究的领域。

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