# An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites
## 2 Background
### AESV Background  
Firefighting techniques are changing over time with the advancement of different sensors for detecting fire and environmental conditions and with the use of artificial intelligence (AI) for smart rescue planning [16; 17]. However, despite the extensive advancement of firefighting, several issues remain to be addressed. As the first responders in fire emergencies, firefighters are subject to health and safety risks in their job. Rescuing victims from the burning sites becomes quite difficult due to the extreme temperatures, low visibility, the roar of flames, and the presence of harmful air pollutants such as carbon monoxide (CO) and carbon dioxide (CO\({}_{2}\)) [18]. These factors increase the risk of cardiac problems among firefighters. Smart firefighting concepts involving various sensors, communication means, and other fire protection and alarm systems pave the way for more intelligent and safer ways to fight fires. An initial development concept of an autonomous embedded system vehicle (AESV) design enabling firefighters to monitor and analyze environmental conditions remotely and produce a 2D-LiDAR map of individual rooms is described in [12]. Figure 2 refers to the outlook of AESV. The AESV design is based on the integration of the sensors and cameras using different embedded system boards. The sensor peripherals process the collected data through a Teensy 3.6 board to transmits the NVIDIA Jetson Nano board. The Teensy board also controls the pulse width modulation (PWM) signals for the motor controller and manages the autonomous vehicle. The Jetson Nano is the central board that supports camera information and runs the Robotic Operating System (ROS) [19] for data telemetry and navigation. The AESV initial design considerations also include multi-AESV units that would need to coordinate the autonomous navigation for data collection, intracommunication between the units, communication to a centralized server, and back-and-forward functionalities. ROS has become the backbone of the AESV design since it manages data collection from each sensor, transmits the data and images, and captures and orchestrates the vehicle's navigation. The audio-based addition for the AESV is a machine learning detection model and the ROS communication of the detection decisions are relayed back to the server. Therefore, developing and implementing a machine learning model that can be effective under these design parameters is essential. Part of the functionality of the AESV design is to use computer vision with machine learning for high-temperature environments. The thick smoke caused by burning makes it extremely difficult for firefighters to look for victims with bare eyes. An effort to create a machine learning model that consists of a convolutional neural network (CNN) architecture  
Figure 2: Autonomous embedded system vehicle (AESV). trained to recognize victims from thermal IR images is presented in [13]. The thermal images were collected via an infrared (IR) camera mounted on the AESV. The CNN-based model did an excellent task of detecting and classifying objects into \"human\", \"pet\", and \"no victims\", with an average accuracy of 94.6%. Despite this superior performance, the model had a few limitations. Fire spreads rapidly in burning sites, resulting in difficult situations such as roofs collapsing and power outages. In industrial areas, fire can cause massive explosions due to explosive materials such as gases and chemicals. All these can damage the structure, and victims are likely to be trapped in confined places. The thermal camera attached to the AESV cannot see through thick objects such as walls or wood. Therefore, the model cannot identify victims trapped inside closed places, leaving out victims in danger. The AESV is designed so that it can move spontaneously in the burning sites, avoiding obstacles. The vehicle is equipped with sensors, a navigation system, and an infrared (IR) camera. However, the vehicle's size is small, measuring approximately four to six inches in height.