  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.
