A method to locate sound sources using an audio recording system mounted on an unmanned aerial vehicle (UAV) is proposed. The method introduces extension algorithms to apply on top of a baseline approach, which performs localisation by estimating the peak signal-to-noise ratio (SNR) response in the time-frequency and angular spectra with the time difference of arrival information. The proposed extensions include a noise reduction and a post-processing algorithm to address the challenges in a UAV setting. The noise reduction algorithm reduces influences of UAV rotor noise on localisation performance, by scaling the SNR response using power spectral density of the UAV rotor noise, estimated using a denoising autoencoder. For the source tracking problem, an angular spectral range restricted peak search and link post-processing algorithm is also proposed to filter out incorrect location estimates along the localisation path. Experimental results show the proposed extensions yielded improvements in locating the target sound source correctly, with a 0.0064–0.175 decrease in mean haversine distance error across various UAV operating scenarios. The proposed method also shows a reduction in unexpected location estimations, with a 0.0037–0.185 decrease in the 0.75 quartile haversine distance error.

中文翻译：

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噪声功率谱密度标度信噪比响应估计与限制范围搜索使用无人机进行声源定位

提出了一种使用安装在无人驾驶飞行器 (UAV) 上的录音系统来定位声源的方法。该方法引入了扩展算法以应用于基线方法之上，该方法通过估计具有到达时间差信息的时频和角谱中的峰值信噪比 (SNR) 响应来执行定位。提议的扩展包括降噪和后处理算法，以解决无人机设置中的挑战。降噪算法通过使用 UAV 旋翼噪声的功率谱密度缩放 SNR 响应来降低 UAV 旋翼噪声对定位性能的影响，使用降噪自编码器估计。对于源头跟踪问题，还提出了一种角度光谱范围受限的峰值搜索和链路后处理算法，以过滤掉沿定位路径的错误位置估计。实验结果表明，所提出的扩展在正确定位目标声源方面产生了改进，在各种无人机操作场景中平均半正弦距离误差减少了 0.0064-0.175。所提出的方法还显示了意外位置估计的减少，0.75 四分位正弦距离误差减少了 0.0037–0.185。