In this survey we are focusing on utilizing drone-based systems for the detection of individuals, particularly by identifying human screams and other distress signals. This study has significant relevance in post-disaster scenarios, including events such as earthquakes, hurricanes, military conflicts, wildfires, and more. These drones are capable of hovering over disaster-stricken areas that may be challenging for rescue teams to access directly. Unmanned aerial vehicles (UAVs), commonly referred to as drones, are frequently deployed for search-and-rescue missions during disaster situations. Typically, drones capture aerial images to assess structural damage and identify the extent of the disaster. They also employ thermal imaging technology to detect body heat signatures, which can help locate individuals. In some cases, larger drones are used to deliver essential supplies to people stranded in isolated disaster-stricken areas. In our discussions, we delve into the unique challenges associated with locating humans through aerial acoustics. The auditory system must distinguish between human cries and sounds that occur naturally, such as animal calls and wind. Additionally, it should be capable of recognizing distinct patterns related to signals like shouting, clapping, or other ways in which people attempt to signal rescue teams. To tackle this challenge, one solution involves harnessing artificial intelligence (AI) to analyze sound frequencies and identify common audio signatures. Deep learning-based networks, such as convolutional neural networks (CNNs), can be trained using these signatures to filter out noise generated by drone motors and other environmental factors. Furthermore, employing signal processing techniques like the direction of arrival (DOA) based on microphone array signals can enhance the precision of tracking the source of human noises.

翻译：本综述聚焦于利用无人机系统检测个体，特别是通过识别人类尖叫声及其他求救信号。该研究在地震、飓风、军事冲突、野火等灾后场景中具有重要应用价值。这些无人机能够悬停于救援队伍难以直接抵达的受灾区域上方。无人驾驶飞行器（UAV，通常称为无人机）常被部署于灾难情境下的搜救任务。典型应用中，无人机通过拍摄航拍图像评估结构损伤并确定灾害范围，同时利用热成像技术检测人体热信号以定位受困者。部分大型无人机还可向孤立受灾区域运送必要物资。本文深入探讨了通过空中声学定位人类所面临的特有挑战：听觉系统需区分人类呼救声与动物叫声、风声等自然声响，并应能识别呼喊、拍手等求救信号的特征模式。为解决这一难题，可利用人工智能（AI）分析声频特征以识别共性音频特征。基于深度学习的网络（如卷积神经网络CNN）可通过这些特征训练，以滤除无人机马达及环境噪声。此外，采用基于麦克风阵列信号的波达方向（DOA）等信号处理技术，可提升人类声源追踪的精确度。